Experimental Evidence for Phase Synchronization Transitions in the Human Cardiorespiratory System

NASA Astrophysics Data System (ADS)

Bartsch, Ronny; Kantelhardt, Jan W.; Penzel, Thomas; Havlin, Shlomo

2007-02-01

Transitions in the dynamics of complex systems can be characterized by changes in the synchronization behavior of their components. Taking the human cardiorespiratory system as an example and using an automated procedure for screening the synchrograms of 112 healthy subjects we study the frequency and the distribution of synchronization episodes under different physiological conditions that occur during sleep. We find that phase synchronization between heartbeat and breathing is significantly enhanced during non-rapid-eye-movement (non-REM) sleep (deep sleep and light sleep) and reduced during REM sleep. Our results suggest that the synchronization is mainly due to a weak influence of the breathing oscillator upon the heartbeat oscillator, which is disturbed in the presence of long-term correlated noise, superimposed by the activity of higher brain regions during REM sleep.

Density waves at the interface of a binary complex plasma

NASA Astrophysics Data System (ADS)

Yang, Li; Schwabe, Mierk; Zhdanov, Sergey; Thomas, Hubertus M.; Lipaev, Andrey M.; Molotkov, Vladimir I.; Fortov, Vladimir E.; Zhang, Jing; Du, Cheng-Ran

2017-01-01

Density waves were studied in a phase-separated binary complex plasma under microgravity conditions. For the big particles, waves were self-excited by the two-stream instability, while for small particles, they were excited by heartbeat instability with the presence of reversed propagating pulses of a different frequency. By studying the dynamics of wave crests at the interface, we recognize a “collision zone” and a “merger zone” before and after the interface, respectively. The results provide a generic picture of wave-wave interaction at the interface between two “mediums”.

Predicting seizures in untreated temporal lobe epilepsy using point-process nonlinear models of heartbeat dynamics.

PubMed

Valenza, G; Romigi, A; Citi, L; Placidi, F; Izzi, F; Albanese, M; Scilingo, E P; Marciani, M G; Duggento, A; Guerrisi, M; Toschi, N; Barbieri, R

2016-08-01

Symptoms of temporal lobe epilepsy (TLE) are frequently associated with autonomic dysregulation, whose underlying biological processes are thought to strongly contribute to sudden unexpected death in epilepsy (SUDEP). While abnormal cardiovascular patterns commonly occur during ictal events, putative patterns of autonomic cardiac effects during pre-ictal (PRE) periods (i.e. periods preceding seizures) are still unknown. In this study, we investigated TLE-related heart rate variability (HRV) through instantaneous, nonlinear estimates of cardiovascular oscillations during inter-ictal (INT) and PRE periods. ECG recordings from 12 patients with TLE were processed to extract standard HRV indices, as well as indices of instantaneous HRV complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra) obtained through definition of inhomogeneous point-process nonlinear models, employing Volterra-Laguerre expansions of linear, quadratic, and cubic kernels. Experimental results demonstrate that the best INT vs. PRE classification performance (balanced accuracy: 73.91%) was achieved only when retaining the time-varying, nonlinear, and non-stationary structure of heartbeat dynamical features. The proposed approach opens novel important avenues in predicting ictal events using information gathered from cardiovascular signals exclusively.

Long-range anticorrelations and non-Gaussian behavior of the heartbeat

NASA Technical Reports Server (NTRS)

Peng, C.-K.; Mietus, J.; Hausdorff, J. M.; Havlin, S.; Stanley, H. E.; Goldberger, A. L.

1993-01-01

We find that the successive increments in the cardiac beat-to-beat intervals of healthy subjects display scale-invariant, long-range anticorrelations (up to 10 exp 4 heart beats). Furthermore, we find that the histogram for the heartbeat intervals increments is well described by a Levy (1991) stable distribution. For a group of subjects with severe heart disease, we find that the distribution is unchanged, but the long-range correlations vanish. Therefore, the different scaling behavior in health and disease must relate to the underlying dynamics of the heartbeat.

Statistics of return intervals between long heartbeat intervals and their usability for online prediction of disorders

NASA Astrophysics Data System (ADS)

Bogachev, Mikhail I.; Kireenkov, Igor S.; Nifontov, Eugene M.; Bunde, Armin

2009-06-01

We study the statistics of return intervals between large heartbeat intervals (above a certain threshold Q) in 24 h records obtained from healthy subjects. We find that both the linear and the nonlinear long-term memory inherent in the heartbeat intervals lead to power-laws in the probability density function PQ(r) of the return intervals. As a consequence, the probability WQ(t; Δt) that at least one large heartbeat interval will occur within the next Δt heartbeat intervals, with an increasing elapsed number of intervals t after the last large heartbeat interval, follows a power-law. Based on these results, we suggest a method of obtaining a priori information about the occurrence of the next large heartbeat interval, and thus to predict it. We show explicitly that the proposed method, which exploits long-term memory, is superior to the conventional precursory pattern recognition technique, which focuses solely on short-term memory. We believe that our results can be straightforwardly extended to obtain more reliable predictions in other physiological signals like blood pressure, as well as in other complex records exhibiting multifractal behaviour, e.g. turbulent flow, precipitation, river flows and network traffic.

Circadian Role in Daily Pattern of Cardiovascular Risk

NASA Astrophysics Data System (ADS)

Ivanov, Plamen Ch.; Hu, Kun; Chen, Zhi; Hilton, Michael F.; Stanley, H. Eugene; Shea, Steven A.

2004-03-01

Numerous epidemiological studies demonstrate that sudden cardiac death, pulmonary embolism, myocardial infarction, and stroke have a 24-hour daily pattern with a broad peak between 9-11am. Such a daily pattern in cardiovascular risk could be attributable to external factors, such as the daily behavior patterns, including sleep-wake cycles and activity levels, or internal factors, such as the endogenous circadian pacemaker. Findings of significant alternations in the temporal organization and nonlinear properties of heartbeat fluctuations with disease and with sleep-wake transitions raise the intriguing possibility that changes in the mechanism of control associated with behavioral sleep-wake transition may be responsible for the increased cardiac instability observed in particular circadian phases. Alternatively, we hypothesize that there is a circadian clock, independent of the sleep-wake cycle, which affects the cardiac dynamics leading to increased cardiovascular risk. We analyzed continuous recordings from healthy subjects during 7 cycles of forced desynchrony routine wherein subjects' sleep-wake cycles are adjusted to 28 hours so that their behaviors occur across all circadian phases. Heartbeat data were divided into one-hour segments. For each segment, we estimated the correlations and the nonlinear properties of the heartbeat fluctuations at the corresponding circadian phase. Since the sleep and wake contributions are equally weighted in our experiment, a change of the properties of the heartbeat dynamics with circadian phase suggest a circadian rhythm. We show significant circadian-mediated alterations in the correlation and nonlinear properties of the heartbeat resembling those observed in patients with heart failure. Remarkably, these dynamical alterations are centered at 60 degrees circadian phase, coinciding with the 9-11am window of cardiac risk.

Multiscale Poincaré plots for visualizing the structure of heartbeat time series.

PubMed

Henriques, Teresa S; Mariani, Sara; Burykin, Anton; Rodrigues, Filipa; Silva, Tiago F; Goldberger, Ary L

2016-02-09

Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincaré (MSP) plots. Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincaré plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. This generalized multiscale approach to Poincaré plots may be useful in visualizing other types of time series.

Predicting Mood Changes in Bipolar Disorder through Heartbeat Nonlinear Dynamics.

PubMed

Valenza, Gaetano; Nardelli, Mimma; Lanata', Antonio; Gentili, Claudio; Bertschy, Gilles; Kosel, Markus; Scilingo, Enzo Pasquale

2016-04-20

Bipolar Disorder (BD) is characterized by an alternation of mood states from depression to (hypo)mania. Mixed states, i.e., a combination of depression and mania symptoms at the same time, can also be present. The diagnosis of this disorder in the current clinical practice is based only on subjective interviews and questionnaires, while no reliable objective psychophysiological markers are available. Furthermore, there are no biological markers predicting BD outcomes, or providing information about the future clinical course of the phenomenon. To overcome this limitation, here we propose a methodology predicting mood changes in BD using heartbeat nonlinear dynamics exclusively, derived from the ECG. Mood changes are here intended as transitioning between two mental states: euthymic state (EUT), i.e., the good affective balance, and non-euthymic (non-EUT) states. Heart Rate Variability (HRV) series from 14 bipolar spectrum patients (age: 33.439.76, age range: 23-54; 6 females) involved in the European project PSYCHE, undergoing whole night ECG monitoring were analyzed. Data were gathered from a wearable system comprised of a comfortable t-shirt with integrated fabric electrodes and sensors able to acquire ECGs. Each patient was monitored twice a week, for 14 weeks, being able to perform normal (unstructured) activities. From each acquisition, the longest artifact-free segment of heartbeat dynamics was selected for further analyses. Sub-segments of 5 minutes of this segment were used to estimate trends of HRV linear and nonlinear dynamics. Considering data from a current observation at day t0, and past observations at days (t1, t2,...,), personalized prediction accuracies in forecasting a mood state (EUT/non-EUT) at day t+1 were 69% on average, reaching values as high as 83.3%. This approach opens to the possibility of predicting mood states in bipolar patients through heartbeat nonlinear dynamics exclusively.

Scaling in nature: From DNA through heartbeats to weather

NASA Astrophysics Data System (ADS)

Havlin, S.; Buldyrev, S. V.; Bunde, A.; Goldberger, A. L.; Ivanov, P. Ch.; Peng, C.-K.; Stanley, H. E.

1999-12-01

The purpose of this talk is to describe some recent progress in applying scaling concepts to various systems in nature. We review several systems characterized by scaling laws such as DNA sequences, heartbeat rates and weather variations. We discuss the finding that the exponent α quantifying the scaling in DNA in smaller for coding than for noncoding sequences. We also discuss the application of fractal scaling analysis to the dynamics of heartbeat regulation, and report the recent finding that the scaling exponent α is smaller during sleep periods compared to wake periods. We also discuss the recent findings that suggest a universal scaling exponent characterizing the weather fluctuations.

Sleep-wake differences in scaling behavior of the human heartbeat: analysis of terrestrial and long-term space flight data

NASA Technical Reports Server (NTRS)

Bunde, A.; Amaral, L. A.; Havlin, S.; Fritsch-Yelle, J.; Baevsky, R. M.; Stanley, H. E.; Goldberger, A. L.

1999-01-01

We compare scaling properties of the cardiac dynamics during sleep and wake periods for healthy individuals, cosmonauts during orbital flight, and subjects with severe heart disease. For all three groups, we find a greater degree of anticorrelation in the heartbeat fluctuations during sleep compared to wake periods. The sleep-wake difference in the scaling exponents for the three groups is comparable to the difference between healthy and diseased individuals. The observed scaling differences are not accounted for simply by different levels of activity, but appear related to intrinsic changes in the neuroautonomic control of the heartbeat.

Scaling in nature: from DNA through heartbeats to weather

NASA Technical Reports Server (NTRS)

Havlin, S.; Buldyrev, S. V.; Bunde, A.; Goldberger, A. L.; Peng, C. K.; Stanley, H. E.

1999-01-01

The purpose of this report is to describe some recent progress in applying scaling concepts to various systems in nature. We review several systems characterized by scaling laws such as DNA sequences, heartbeat rates and weather variations. We discuss the finding that the exponent alpha quantifying the scaling in DNA in smaller for coding than for noncoding sequences. We also discuss the application of fractal scaling analysis to the dynamics of heartbeat regulation, and report the recent finding that the scaling exponent alpha is smaller during sleep periods compared to wake periods. We also discuss the recent findings that suggest a universal scaling exponent characterizing the weather fluctuations.

Evaluation of Sleep by Detrended Fluctuation Analysis of the Heartbeat

NASA Astrophysics Data System (ADS)

Yazawa, Toru; Shimoda, Yukio; Hutapea, Albert M.

2011-08-01

There are already-established methods for investigating biological signals such as rhythmic heartbeats. We used detrended fluctuation analysis (DFA), originally developed by Peng et al. (1995) to check power-law characteristics, because the method can quantify the heart condition numerically. In this article, we studied the heartbeat of sleeping subjects. Our purpose was to test whether DFA is useful to evaluate the subject's wellness of both during being awake and sleeping. This is a challenge to measure sleep without complex/expensive machine, an electro encephalography (EEG). We conducted electrophysiological recording to measure heartbeats during sleep using electrocardiograph with three-leads, one ground electrode and two active electrodes attached to chest. For good recording, a stable baseline must be maintained even when subjects move their body. We needed a tool to ensure long-term steady recording. We thus invented a new electric-circuit designed to produce this desired result. This gadget allowed us to perform heartbeat recording without any drifting baseline. We then were able to detect 100% of heartbeat peaks over the entire period of sleep. Here, we show a case study as empirical evidence that DFA is useful numerical method for quantifying sleep by using the scaling exponents.

Circadian clock and cardiac vulnerability: A time stamp on multi-scale neuroautonomic regulation

NASA Astrophysics Data System (ADS)

Ivanov, Plamen Ch.

2005-03-01

Cardiovascular vulnerability displays a 24-hour pattern with a peak between 9AM and 11AM. This daily pattern in cardiac risk is traditionally attributed to external factors including activity levels and sleep-wake cycles. However,influences from the endogenous circadian pacemaker independent from behaviors may also affect cardiac control. We investigate heartbeat dynamics in healthy subjects recorded throughout a 10-day protocol wherein the sleep/wake and behavior cycles are desynchronized from the endogenous circadian cycle,enabling assessment of circadian factors while controlling for behavior-related factors. We demonstrate that the scaling exponent characterizing temporal correlations in heartbeat dynamics over multiple time scales does exhibit a significant circadian rhythm with a sharp peak at the circadian phase corresponding to the period 9-11AM, and that this rhythm is independent from scheduled behaviors and mean heart rate. Our findings of strong circadian rhythms in the multi-scale heartbeat dynamics of healthy young subjects indicate that the underlying mechanism of cardiac regulation is strongly influenced by the endogenous circadian pacemaker. A similar circadian effect in vulnerable individuals with underlying cardiovascular disease would contribute to the morning peak of adverse cardiac events observed in epidemiological studies.

New Methods for the Analysis of Heartbeat Behavior in Risk Stratification

PubMed Central

Glass, Leon; Lerma, Claudia; Shrier, Alvin

2011-01-01

Developing better methods for risk stratification for tachyarrhythmic sudden cardiac remains a major challenge for physicians and scientists. Since the transition from sinus rhythm to ventricular tachycardia/fibrillation happens by different mechanisms in different people, it is unrealistic to think that a single measure will be adequate to provide a good index for risk stratification. We analyze the dynamical properties of ventricular premature complexes over 24 h in an effort to understand the underlying mechanisms of ventricular arrhythmias and to better understand the arrhythmias that occur in individual patients. Two dimensional density plots, called heartprints, correlate characteristic features of the dynamics of premature ventricular complexes and the sinus rate. Heartprints show distinctive characteristics in individual patients. Based on a better understanding of the natures of transitions from sinus rhythm to sudden cardiac and the mechanisms of arrhythmia prior to cardiac arrest, it should be possible to develop better methods for risk stratification. PMID:22144963

Inhomogeneous point-process entropy: An instantaneous measure of complexity in discrete systems

NASA Astrophysics Data System (ADS)

Valenza, Gaetano; Citi, Luca; Scilingo, Enzo Pasquale; Barbieri, Riccardo

2014-05-01

Measures of entropy have been widely used to characterize complexity, particularly in physiological dynamical systems modeled in discrete time. Current approaches associate these measures to finite single values within an observation window, thus not being able to characterize the system evolution at each moment in time. Here, we propose a new definition of approximate and sample entropy based on the inhomogeneous point-process theory. The discrete time series is modeled through probability density functions, which characterize and predict the time until the next event occurs as a function of the past history. Laguerre expansions of the Wiener-Volterra autoregressive terms account for the long-term nonlinear information. As the proposed measures of entropy are instantaneously defined through probability functions, the novel indices are able to provide instantaneous tracking of the system complexity. The new measures are tested on synthetic data, as well as on real data gathered from heartbeat dynamics of healthy subjects and patients with cardiac heart failure and gait recordings from short walks of young and elderly subjects. Results show that instantaneous complexity is able to effectively track the system dynamics and is not affected by statistical noise properties.

HEARTBEAT STARS: SPECTROSCOPIC ORBITAL SOLUTIONS FOR SIX ECCENTRIC BINARY SYSTEMS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smullen, Rachel A.; Kobulnicky, Henry A., E-mail: rsmullen@email.arizona.edu

2015-08-01

We present multi-epoch spectroscopy of “heartbeat stars,” eccentric binaries with dynamic tidal distortions and tidally induced pulsations originally discovered with the Kepler satellite. Optical spectra of six known heartbeat stars using the Wyoming Infrared Observatory 2.3 m telescope allow measurement of stellar effective temperatures and radial velocities from which we determine orbital parameters including the periods, eccentricities, approximate mass ratios, and component masses. These spectroscopic solutions confirm that the stars are members of eccentric binary systems with eccentricities e > 0.34 and periods P = 7–20 days, strengthening conclusions from prior works that utilized purely photometric methods. Heartbeat stars inmore » this sample have A- or F-type primary components. Constraints on orbital inclinations indicate that four of the six systems have minimum mass ratios q = 0.3–0.5, implying that most secondaries are probable M dwarfs or earlier. One system is an eclipsing, double-lined spectroscopic binary with roughly equal-mass mid-A components (q = 0.95), while another shows double-lined behavior only near periastron, indicating that the F0V primary has a G1V secondary (q = 0.65). This work constitutes the first measurements of the masses of secondaries in a statistical sample of heartbeat stars. The good agreement between our spectroscopic orbital elements and those derived using a photometric model support the idea that photometric data are sufficient to derive reliable orbital parameters for heartbeat stars.« less

Stratification Pattern of Static and Scale-Invariant Dynamic Measures of Heartbeat Fluctuations Across Sleep Stages in Young and Elderly

PubMed Central

Schmitt, Daniel T.; Stein, Phyllis K.; Ivanov, Plamen Ch.

2010-01-01

Cardiac dynamics exhibit complex variability characterized by scale-invariant and nonlinear temporal organization related to the mechanism of neuroautonomic control, which changes with physiologic states and pathologic conditions. Changes in sleep regulation during sleep stages are also related to fluctuations in autonomic nervous activity. However, the interaction between sleep regulation and cardiac autonomic control remains not well understood. Even less is known how this interaction changes with age, as aspects of both cardiac dynamics and sleep regulation differ in healthy elderly compared to young subjects. We hypothesize that because of the neuroautonomic responsiveness in young subjects, fractal and nonlinear features of cardiac dynamics exhibit a pronounced stratification pattern across sleep stages, while in elderly these features will remain unchanged due to age-related loss of cardiac variability and decline of neuroautonomic responsiveness. We analyze the variability and the temporal fractal organization of heartbeat fluctuations across sleep stages in both young and elderly. We find that independent linear and nonlinear measures of cardiac control consistently exhibit the same ordering in their values across sleep stages, forming a robust stratification pattern. Despite changes in sleep architecture and reduced heart rate variability in elderly subjects, this stratification surprisingly does not break down with advanced age. Moreover, the difference between sleep stages for some linear, fractal, and nonlinear measures exceeds the difference between young and elderly, suggesting that the effect of sleep regulation on cardiac dynamics is significantly stronger than the effect of healthy aging. Quantifying changes in this stratification pattern may provide insights into how alterations in sleep regulation contribute to increased cardiac risk. PMID:19203874

Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT-BIH databases.

PubMed

Carrara, Marta; Carozzi, Luca; Moss, Travis J; de Pasquale, Marco; Cerutti, Sergio; Lake, Douglas E; Moorman, J Randall; Ferrario, Manuela

2015-01-01

Identification of atrial fibrillation (AF) is a clinical imperative. Heartbeat interval time series are increasingly available from personal monitors, allowing new opportunity for AF diagnosis. Previously, we devised numerical algorithms for identification of normal sinus rhythm (NSR), AF, and SR with frequent ectopy using dynamical measures of heart rate. Here, we wished to validate them in the canonical MIT-BIH ECG databases. We tested algorithms on the NSR, AF and arrhythmia databases. When the databases were combined, the positive predictive value of the new algorithms exceeded 95% for NSR and AF, and was 40% for SR with ectopy. Further, dynamical measures did not distinguish atrial from ventricular ectopy. Inspection of individual 24hour records showed good correlation of observed and predicted rhythms. Heart rate dynamical measures are effective ingredients in numerical algorithms to classify cardiac rhythm from the heartbeat intervals time series alone. Copyright © 2015 Elsevier Inc. All rights reserved.

Long- and short-time analysis of heartbeat sequences: correlation with mortality risk in congestive heart failure patients.

PubMed

Allegrini, P; Balocchi, R; Chillemi, S; Grigolini, P; Hamilton, P; Maestri, R; Palatella, L; Raffaelli, G

2003-06-01

We analyze RR heartbeat sequences with a dynamic model that satisfactorily reproduces both the long- and the short-time statistical properties of heart beating. These properties are expressed quantitatively by means of two significant parameters, the scaling delta concerning the asymptotic effects of long-range correlation, and the quantity 1-pi establishing the amount of uncorrelated fluctuations. We find a correlation between the position in the phase space (delta, pi) of patients with congestive heart failure and their mortality risk.

Fractals in biology and medicine

NASA Technical Reports Server (NTRS)

Havlin, S.; Buldyrev, S. V.; Goldberger, A. L.; Mantegna, R. N.; Ossadnik, S. M.; Peng, C. K.; Simons, M.; Stanley, H. E.

1995-01-01

Our purpose is to describe some recent progress in applying fractal concepts to systems of relevance to biology and medicine. We review several biological systems characterized by fractal geometry, with a particular focus on the long-range power-law correlations found recently in DNA sequences containing noncoding material. Furthermore, we discuss the finding that the exponent alpha quantifying these long-range correlations ("fractal complexity") is smaller for coding than for noncoding sequences. We also discuss the application of fractal scaling analysis to the dynamics of heartbeat regulation, and report the recent finding that the normal heart is characterized by long-range "anticorrelations" which are absent in the diseased heart.

Analysis of Instantaneous Linear, Nonlinear and Complex Cardiovascular Dynamics from Videophotoplethysmography.

PubMed

Valenza, Gaetano; Iozzia, Luca; Cerina, Luca; Mainardi, Luca; Barbieri, Riccardo

2018-05-01

There is a fast growing interest in the use of non-contact devices for health and performance assessment in humans. In particular, the use of non-contact videophotoplethysmography (vPPG) has been recently demonstrated as a feasible way to extract cardiovascular information. Nevertheless, proper validation of vPPG-derived heartbeat dynamics is still missing. We aim to an in-depth validation of time-varying, linear and nonlinear/complex dynamics of the pulse rate variability extracted from vPPG. We apply inhomogeneous pointprocess nonlinear models to assess instantaneous measures defined in the time, frequency, and bispectral domains as estimated through vPPG and standard ECG. Instantaneous complexity measures, such as the instantaneous Lyapunov exponents and the recently defined inhomogeneous point-process approximate and sample entropy, were estimated as well. Video recordings were processed using our recently proposed method based on zerophase principal component analysis. Experimental data were gathered from 60 young healthy subjects (age: 24±3 years) undergoing postural changes (rest-to-stand maneuver). Group averaged results show that there is an overall agreement between linear and nonlinear/complexity indices computed from ECG and vPPG during resting state conditions. However, important differences are found, particularly in the bispectral and complexity domains, in recordings where the subjects has been instructed to stand up. Although significant differences exist between cardiovascular estimates from vPPG and ECG, it is very promising that instantaneous sympathovagal changes, as well as time-varying complex dynamics, were correctly identified, especially during resting state. In addition to a further improvement of the video signal quality, more research is advocated towards a more precise estimation of cardiovascular dynamics by a comprehensive nonlinear/complex paradigm specifically tailored to the non-contact quantification. Schattauer GmbH.

Aging and cardiovascular complexity: effect of the length of RR tachograms

PubMed Central

Nagaraj, Nithin

2016-01-01

As we age, our hearts undergo changes that result in a reduction in complexity of physiological interactions between different control mechanisms. This results in a potential risk of cardiovascular diseases which are the number one cause of death globally. Since cardiac signals are nonstationary and nonlinear in nature, complexity measures are better suited to handle such data. In this study, three complexity measures are used, namely Lempel–Ziv complexity (LZ), Sample Entropy (SampEn) and Effort-To-Compress (ETC). We determined the minimum length of RR tachogram required for characterizing complexity of healthy young and healthy old hearts. All the three measures indicated significantly lower complexity values for older subjects than younger ones. However, the minimum length of heart-beat interval data needed differs for the three measures, with LZ and ETC needing as low as 10 samples, whereas SampEn requires at least 80 samples. Our study indicates that complexity measures such as LZ and ETC are good candidates for the analysis of cardiovascular dynamics since they are able to work with very short RR tachograms. PMID:27957395

Real-time biomimetic Central Pattern Generators in an FPGA for hybrid experiments

PubMed Central

Ambroise, Matthieu; Levi, Timothée; Joucla, Sébastien; Yvert, Blaise; Saïghi, Sylvain

2013-01-01

This investigation of the leech heartbeat neural network system led to the development of a low resources, real-time, biomimetic digital hardware for use in hybrid experiments. The leech heartbeat neural network is one of the simplest central pattern generators (CPG). In biology, CPG provide the rhythmic bursts of spikes that form the basis for all muscle contraction orders (heartbeat) and locomotion (walking, running, etc.). The leech neural network system was previously investigated and this CPG formalized in the Hodgkin–Huxley neural model (HH), the most complex devised to date. However, the resources required for a neural model are proportional to its complexity. In response to this issue, this article describes a biomimetic implementation of a network of 240 CPGs in an FPGA (Field Programmable Gate Array), using a simple model (Izhikevich) and proposes a new synapse model: activity-dependent depression synapse. The network implementation architecture operates on a single computation core. This digital system works in real-time, requires few resources, and has the same bursting activity behavior as the complex model. The implementation of this CPG was initially validated by comparing it with a simulation of the complex model. Its activity was then matched with pharmacological data from the rat spinal cord activity. This digital system opens the way for future hybrid experiments and represents an important step toward hybridization of biological tissue and artificial neural networks. This CPG network is also likely to be useful for mimicking the locomotion activity of various animals and developing hybrid experiments for neuroprosthesis development. PMID:24319408

Beyond Fractals and 1/f Noise: Multifractal Analysis of Complex Physiological Time Series

NASA Astrophysics Data System (ADS)

Ivanov, Plamen Ch.; Amaral, Luis A. N.; Ashkenazy, Yosef; Stanley, H. Eugene; Goldberger, Ary L.; Hausdorff, Jeffrey M.; Yoneyama, Mitsuru; Arai, Kuniharu

2001-03-01

We investigate time series with 1/f-like spectra generated by two physiologic control systems --- the human heartbeat and human gait. We show that physiological fluctuations exhibit unexpected ``hidden'' structures often described by scaling laws. In particular, our studies indicate that when analyzed on different time scales the heartbeat fluctuations exhibit cascades of branching patterns with self-similar (fractal) properties, characterized by long-range power-law anticorrelations. We find that these scaling features change during sleep and wake phases, and with pathological perturbations. Further, by means of a new wavelet-based technique, we find evidence of multifractality in the healthy human heartbeat even under resting conditions, and show that the multifractal character and nonlinear properties of the healthy heart are encoded in the Fourier phases. We uncover a loss of multifractality for a life-threatening condition, congestive heart failure. In contrast to the heartbeat, we find that the interstride interval time series of healthy human gait, a voluntary process under neural regulation, is described by a single fractal dimension (such as classical 1/f noise) indicating monofractal behavior. Thus our approach can help distinguish physiological and physical signals with comparable frequency spectra and two-point correlations, and guide modeling of their control mechanisms.

[Biometric identification method for ECG based on the piecewise linear representation (PLR) and dynamic time warping (DTW)].

PubMed

Yang, Licai; Shen, Jun; Bao, Shudi; Wei, Shoushui

2013-10-01

To treat the problem of identification performance and the complexity of the algorithm, we proposed a piecewise linear representation and dynamic time warping (PLR-DTW) method for ECG biometric identification. Firstly we detected R peaks to get the heartbeats after denoising preprocessing. Then we used the PLR method to keep important information of an ECG signal segment while reducing the data dimension at the same time. The improved DTW method was used for similarity measurements between the test data and the templates. The performance evaluation was carried out on the two ECG databases: PTB and MIT-BIH. The analystic results showed that compared to the discrete wavelet transform method, the proposed PLR-DTW method achieved a higher accuracy rate which is nearly 8% of rising, and saved about 30% operation time, and this demonstrated that the proposed method could provide a better performance.

Interoceptive signals impact visual processing: Cardiac modulation of visual body perception.

PubMed

Ronchi, Roberta; Bernasconi, Fosco; Pfeiffer, Christian; Bello-Ruiz, Javier; Kaliuzhna, Mariia; Blanke, Olaf

2017-09-01

Multisensory perception research has largely focused on exteroceptive signals, but recent evidence has revealed the integration of interoceptive signals with exteroceptive information. Such research revealed that heartbeat signals affect sensory (e.g., visual) processing: however, it is unknown how they impact the perception of body images. Here we linked our participants' heartbeat to visual stimuli and investigated the spatio-temporal brain dynamics of cardio-visual stimulation on the processing of human body images. We recorded visual evoked potentials with 64-channel electroencephalography while showing a body or a scrambled-body (control) that appeared at the frequency of the on-line recorded participants' heartbeat or not (not-synchronous, control). Extending earlier studies, we found a body-independent effect, with cardiac signals enhancing visual processing during two time periods (77-130 ms and 145-246 ms). Within the second (later) time-window we detected a second effect characterised by enhanced activity in parietal, temporo-occipital, inferior frontal, and right basal ganglia-insula regions, but only when non-scrambled body images were flashed synchronously with the heartbeat (208-224 ms). In conclusion, our results highlight the role of interoceptive information for the visual processing of human body pictures within a network integrating cardio-visual signals of relevance for perceptual and cognitive aspects of visual body processing. Copyright © 2017 Elsevier Inc. All rights reserved.

Fractal mechanisms and heart rate dynamics. Long-range correlations and their breakdown with disease

NASA Technical Reports Server (NTRS)

Peng, C. K.; Havlin, S.; Hausdorff, J. M.; Mietus, J. E.; Stanley, H. E.; Goldberger, A. L.

1995-01-01

Under healthy conditions, the normal cardiac (sinus) interbeat interval fluctuates in a complex manner. Quantitative analysis using techniques adapted from statistical physics reveals the presence of long-range power-law correlations extending over thousands of heartbeats. This scale-invariant (fractal) behavior suggests that the regulatory system generating these fluctuations is operating far from equilibrium. In contrast, it is found that for subjects at high risk of sudden death (e.g., congestive heart failure patients), these long-range correlations break down. Application of fractal scaling analysis and related techniques provides new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as motivating development of novel physiologic models of systems that appear to be heterodynamic rather than homeostatic.

Radar sensitivity to human heartbeats and respiration

NASA Astrophysics Data System (ADS)

Aardal, Øyvind; Brovoll, Sverre; Paichard, Yoann; Berger, Tor; Lande, Tor Sverre; Hamran, Svein-Erik

2015-05-01

Human heartbeats and respiration can be detected from a distance using radar. This can be used for medical applications and human being detection. It is useful to have a system independent measure of how detectable the vital signs are. In radar applications, the Radar Cross Section (RCS) is normally used to characterize the detectability of an object. Since the human vital signs are seen by the radar as movements of the torso, the modulations in the person RCS can be used as a system independent measure of the vital signs detectability. In this paper, measurements of persons seated in an anechoic chamber are presented. The measurements were calibrated using empty room and a metallic calibration sphere. A narrowband radar operating at frequencies from 500 MHz to 18 GHz in discrete steps was used. A turntable provided measurements at precise aspect angles all around the person under test. In an I & Q receiver, the heartbeat and respiration modulation is a combination of amplitude and phase mod- modulations. The measurements were filtered, leaving the modulations from the vital signs in the radar recordings. The procedure for RCS computation was applied to these filtered data, capturing the complex signatures. It was found that both the heartbeat and respiration detectability increase with increasing frequency. The heartbeat signatures are almost equal from the front and the back, while being almost undetectable from the sides of the person. The respiration signatures are slightly higher from the front than from the back, and smaller from the sides. The signature measurements presented in this paper provide an objective system independent measure of the detectability of human vital signs as a function of frequency and aspect angle. These measures are useful for example in system design and in assessing real measurement scenarios.

Aging Effects on Cardiac and Respiratory Dynamics in Healthy Subjects across Sleep Stages

PubMed Central

Schumann, Aicko Y.; Bartsch, Ronny P.; Penzel, Thomas; Ivanov, Plamen Ch.; Kantelhardt, Jan W.

2010-01-01

Study Objectives: Respiratory and heart rate variability exhibit fractal scaling behavior on certain time scales. We studied the short-term and long-term correlation properties of heartbeat and breathing-interval data from disease-free subjects focusing on the age-dependent fractal organization. We also studied differences across sleep stages and night-time wake and investigated quasi-periodic variations associated with cardiac risk. Design: Full-night polysomnograms were recorded during 2 nights, including electrocardiogram and oronasal airflow. Setting: Data were collected in 7 laboratories in 5 European countries. Participants: 180 subjects without health complaints (85 males, 95 females) aged from 20 to 89 years. Interventions: None. Measurements and Results: Short-term correlations in heartbeat intervals measured by the detrended fluctuation analysis (DFA) exponent α1 show characteristic age dependence with a maximum around 50–60 years disregarding the dependence on sleep and wake states. Long-term correlations measured by α2 differ in NREM sleep when compared with REM sleep and wake, besides weak age dependence. Results for respiratory intervals are similar to those for α2 of heartbeat intervals. Deceleration capacity (DC) decreases with age; it is lower during REM and deep sleep (compared with light sleep and wake). Conclusion: The age dependence of α1 should be considered when using this value for diagnostic purposes in post-infarction patients. Pronounced long-term correlations (larger α2) for heartbeat and respiration during REM sleep and wake indicate an enhanced control of higher brain regions, which is absent during NREM sleep. Reduced DC possibly indicates an increased cardiovascular risk with aging and during REM and deep sleep. Citation: Schumann AY; Bartsch RP; Penzel T; Ivanov PC; Kantelhardt JW. Aging effects on cardiac and respiratory dynamics in healthy subjects across sleep stages. SLEEP 2010;33(7):943-955. PMID:20614854

Detection of premature ventricular contractions on a ventricular electrocardiogram for patients with left ventricular assist devices.

PubMed

Park, Sung Min; Lee, Jin Hong; Choi, Seong Wook

2014-12-01

The ventricular electrocardiogram (v-ECG) was developed for long-term monitoring of heartbeats in patients with a left ventricular assist device (LVAD) and does not normally have the functionality necessary to detect additional heart irregularities that can progress to critical arrhythmias. Although the v-ECG has the benefits of physiological optimization and counterpulsation control, when abnormal heartbeats occur, the v-ECG does not show the distinct abnormal waveform that enables easy detection of an abnormal heartbeat among normal heartbeats on the conventional ECG. In this study, the v-ECGs of normal and abnormal heartbeats are compared with each other with respect to peak-to-peak voltage, area, and maximal slopes, and a new method to detect abnormal heartbeats is suggested. In a series of animal experiments with three porcine models (Yorkshire pigs weighing 30-40 kg), a v-ECG and conventional ECG were taken simultaneously during LVAD perfusion. Clinical experts found 104 abnormal heartbeats from the saved conventional ECG data and confirmed that the other 3159 heartbeats were normal. Almost all of the abnormal heartbeats were premature ventricular contractions (PVCs), and there was short-term tachycardia for 3 s. A personal computer was used to automatically detect abnormal heartbeats with the v-ECG according to the new method, and its results were compared with the clinicians' results. The new method found abnormal heartbeats with 90% accuracy, and less than 15% of the total PVCs were missed. Copyright © 2014 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Scale-Free Neural and Physiological Dynamics in Naturalistic Stimuli Processing

PubMed Central

Lin, Amy

2016-01-01

Abstract Neural activity recorded at multiple spatiotemporal scales is dominated by arrhythmic fluctuations without a characteristic temporal periodicity. Such activity often exhibits a 1/f-type power spectrum, in which power falls off with increasing frequency following a power-law function: P(f)∝1/fβ, which is indicative of scale-free dynamics. Two extensively studied forms of scale-free neural dynamics in the human brain are slow cortical potentials (SCPs)—the low-frequency (<5 Hz) component of brain field potentials—and the amplitude fluctuations of α oscillations, both of which have been shown to carry important functional roles. In addition, scale-free dynamics characterize normal human physiology such as heartbeat dynamics. However, the exact relationships among these scale-free neural and physiological dynamics remain unclear. We recorded simultaneous magnetoencephalography and electrocardiography in healthy subjects in the resting state and while performing a discrimination task on scale-free dynamical auditory stimuli that followed different scale-free statistics. We observed that long-range temporal correlation (captured by the power-law exponent β) in SCPs positively correlated with that of heartbeat dynamics across time within an individual and negatively correlated with that of α-amplitude fluctuations across individuals. In addition, across individuals, long-range temporal correlation of both SCP and α-oscillation amplitude predicted subjects’ discrimination performance in the auditory task, albeit through antagonistic relationships. These findings reveal interrelations among different scale-free neural and physiological dynamics and initial evidence for the involvement of scale-free neural dynamics in the processing of natural stimuli, which often exhibit scale-free dynamics. PMID:27822495

Ultra-Wideband Chaos Life-Detection Radar with Sinusoidal Wave Modulation

NASA Astrophysics Data System (ADS)

Xu, Hang; Li, Ying; Zhang, Jianguo; Han, Hong; Zhang, Bing; Wang, Longsheng; Wang, Yuncai; Wang, Anbang

2017-12-01

We propose and experimentally demonstrate an ultra-wideband (UWB) chaos life-detection radar. The proposed radar transmits a wideband chaotic-pulse-position modulation (CPPM) signal modulated by a single-tone sinusoidal wave. A narrow-band split ring sensor is used to collect the reflected sinusoidal wave, and a lock-in amplifier is utilized to identify frequencies of respiration and heartbeat by detecting the phase change of the sinusoidal echo signal. Meanwhile, human location is realized by correlating the CPPM echo signal with its delayed duplicate and combining the synthetic aperture technology. Experimental results demonstrate that the human target can be located accurately and his vital signs can be detected in a large dynamic range through a 20-cm-thick wall using our radar system. The down-range resolution is 15cm, benefiting from the 1-GHz bandwidth of the CPPM signal. The dynamic range for human location is 50dB, and the dynamic ranges for heartbeat and respiration detection respectively are 20dB and 60dB in our radar system. In addition, the bandwidth of the CPPM signal can be adjusted from 620MHz to 1.56GHz to adapt to different requirements.

A Unified Point Process Probabilistic Framework to Assess Heartbeat Dynamics and Autonomic Cardiovascular Control

PubMed Central

Chen, Zhe; Purdon, Patrick L.; Brown, Emery N.; Barbieri, Riccardo

2012-01-01

In recent years, time-varying inhomogeneous point process models have been introduced for assessment of instantaneous heartbeat dynamics as well as specific cardiovascular control mechanisms and hemodynamics. Assessment of the model’s statistics is established through the Wiener-Volterra theory and a multivariate autoregressive (AR) structure. A variety of instantaneous cardiovascular metrics, such as heart rate (HR), heart rate variability (HRV), respiratory sinus arrhythmia (RSA), and baroreceptor-cardiac reflex (baroreflex) sensitivity (BRS), are derived within a parametric framework and instantaneously updated with adaptive and local maximum likelihood estimation algorithms. Inclusion of second-order non-linearities, with subsequent bispectral quantification in the frequency domain, further allows for definition of instantaneous metrics of non-linearity. We here present a comprehensive review of the devised methods as applied to experimental recordings from healthy subjects during propofol anesthesia. Collective results reveal interesting dynamic trends across the different pharmacological interventions operated within each anesthesia session, confirming the ability of the algorithm to track important changes in cardiorespiratory elicited interactions, and pointing at our mathematical approach as a promising monitoring tool for an accurate, non-invasive assessment in clinical practice. We also discuss the limitations and other alternative modeling strategies of our point process approach. PMID:22375120

Quantifying memory in complex physiological time-series.

PubMed

Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

Quantifying Memory in Complex Physiological Time-Series

PubMed Central

Shirazi, Amir H.; Raoufy, Mohammad R.; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R.; Amodio, Piero; Jafari, G. Reza; Montagnese, Sara; Mani, Ali R.

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of “memory length” was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are ‘forgotten’ quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations. PMID:24039811

Slow Heartbeat

MedlinePlus

... Back to Symptoms & Diagnosis Slow Heartbeat Slow heartbeat (heart rate), called bradycardia , is an arrhythmia , or disorder of ... 60 to 100 times a minute. Changes in heart rate caused by activity, diet, medications, and age are ...

Development and Evaluation of Heartbeat: A Machine Perfusion Heart Preservation System.

PubMed

Li, Yongnan; Zeng, Qingdong; Liu, Gang; Du, Junzhe; Gao, Bingren; Wang, Wei; Zheng, Zhe; Hu, Shengshou; Ji, Bingyang

2017-11-01

Static cold storage is accompanied with a partial safe ischemic interval for donor hearts. In this current study, a machine perfusion system was built to provide a better preservation for the donor heart and assessment for myocardial function. Chinese mini-swine (weight 30-35 kg, n = 16) were randomly divided into HTK, Celsior, and Heartbeat groups. All donor hearts were respectively preserved for 8 hours under static cold storage or machine perfusion. The perfusion solution is aimed to maintain its homeostasis based on monitoring the Heartbeat group. The ultrastructure of myocardium suggests better myocardial protection in the Heartbeat group compared with HTK or Celsior-preserved hearts. The myocardial and coronary artery structural and functional integrity was evaluated by immunofluorescence and Western blots in the Heartbeat. In the Heartbeat group, donor hearts maintained a high adenosine triphosphate level. Bcl-2 and Beclin-1 protein demonstrates high expression in the Celsior group. The Heartbeat system can be used to preserve donor hearts, and it could guarantee the myocardial and endothelial function of hearts during machine perfusion. Translating Heartbeat into clinical practice, it is such as to impact on donor heart preservation for cardiac transplantation. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Simultaneous Measurement of Breathing and Heartbeat using Airborne Ultrasound in a Standing Position

NASA Astrophysics Data System (ADS)

Hoshiba, Kotaro; Hirata, Shinnosuke; Hachiya, Hiroyuki

We have been studied about non-contact measurement of respiration and heart rates. In previous papers, the measurement system of small velocity using the M-sequence-modulated signal and phase difference of reflected signals from the target has been proposed. In this paper, we describe measurement of breathing and heartbeat in a standing position using the proposed method. Body-surface velocities by breathing and heartbeat could be observed respectively when the volunteer was breathing and holding the breath. In addition, measured velocity of breathing volunteer includes the component by heartbeat. It is considered that it has possibility to measure breathing and heartbeat concurrently.

Anxiety sensitivity and auditory perception of heartbeat.

PubMed

Pollock, R A; Carter, A S; Amir, N; Marks, L E

2006-12-01

Anxiety sensitivity (AS) is the fear of sensations associated with autonomic arousal. AS has been associated with the development and maintenance of panic disorder. Given that panic patients often rate cardiac symptoms as the most fear-provoking feature of a panic attack, AS individuals may be especially responsive to cardiac stimuli. Consequently, we developed a signal-in-white-noise detection paradigm to examine the strategies that high and low AS individuals use to detect and discriminate normal and abnormal heartbeat sounds. Compared to low AS individuals, high AS individuals demonstrated a greater propensity to report the presence of normal, but not abnormal, heartbeat sounds. High and low AS individuals did not differ in their ability to perceive normal heartbeat sounds against a background of white noise; however, high AS individuals consistently demonstrated lower ability to discriminate abnormal heartbeats from background noise and between abnormal and normal heartbeats. AS was characterized by an elevated false alarm rate across all tasks. These results suggest that heartbeat sounds may be fear-relevant cues for AS individuals, and may affect their attention and perception in tasks involving threat signals.

Optimal frequency range for medical radar measurements of human heartbeats using body-contact radar.

PubMed

Brovoll, Sverre; Aardal, Øyvind; Paichard, Yoann; Berger, Tor; Lande, Tor Sverre; Hamran, Svein-Erik

2013-01-01

In this paper the optimal frequency range for heartbeat measurements using body-contact radar is experimentally evaluated. A Body-contact radar senses electromagnetic waves that have penetrated the human body, but the range of frequencies that can be used are limited by the electric properties of the human tissue. The optimal frequency range is an important property needed for the design of body-contact radar systems for heartbeat measurements. In this study heartbeats are measured using three different antennas at discrete frequencies from 0.1 - 10 GHz, and the strength of the received heartbeat signal is calculated. To characterize the antennas, when in contact with the body, two port S-parameters(†) are measured for the antennas using a pork rib as a phantom for the human body. The results shows that frequencies up to 2.5 GHz can be used for heartbeat measurements with body-contact radar.

Rhythm-based heartbeat duration normalization for atrial fibrillation detection.

PubMed

Islam, Md Saiful; Ammour, Nassim; Alajlan, Naif; Aboalsamh, Hatim

2016-05-01

Screening of atrial fibrillation (AF) for high-risk patients including all patients aged 65 years and older is important for prevention of risk of stroke. Different technologies such as modified blood pressure monitor, single lead ECG-based finger-probe, and smart phone using plethysmogram signal have been emerging for this purpose. All these technologies use irregularity of heartbeat duration as a feature for AF detection. We have investigated a normalization method of heartbeat duration for improved AF detection. AF is an arrhythmia in which heartbeat duration generally becomes irregularly irregular. From a window of heartbeat duration, we estimate the possible rhythm of the majority of heartbeats and normalize duration of all heartbeats in the window based on the rhythm so that we can measure the irregularity of heartbeats for both AF and non-AF rhythms in the same scale. Irregularity is measured by the entropy of distribution of the normalized duration. Then we classify a window of heartbeats as AF or non-AF by thresholding the measured irregularity. The effect of this normalization is evaluated by comparing AF detection performances using duration with the normalization, without normalization, and with other existing normalizations. Sensitivity and specificity of AF detection using normalized heartbeat duration were tested on two landmark databases available online and compared with results of other methods (with/without normalization) by receiver operating characteristic (ROC) curves. ROC analysis showed that the normalization was able to improve the performance of AF detection and it was consistent for a wide range of sensitivity and specificity for use of different thresholds. Detection accuracy was also computed for equal rates of sensitivity and specificity for different methods. Using normalized heartbeat duration, we obtained 96.38% accuracy which is more than 4% improvement compared to AF detection without normalization. The proposed normalization method was found useful for improving performance and robustness of AF detection. Incorporation of this method in a screening device could be crucial to reduce the risk of AF-related stroke. In general, the incorporation of the rhythm-based normalization in an AF detection method seems important for developing a robust AF screening device. Copyright © 2016 Elsevier Ltd. All rights reserved.

Econonatology: the physics of the economy in labour

NASA Astrophysics Data System (ADS)

Struzik, Zbigniew R.

2003-06-01

A fear of crashes preoccupies stock market observers, both investors and economic decision makers (governments, banks). This is reflected in major efforts to predict future stock values. However, an economic system under prolonged stress may have in its nature to plunge to lower performance levels but recover without suffering damage. To support this claim, we draw parallels between two complex systems: that of the heart, as observed through the rate of heartbeat, and the economy, measured by the stock index record. The ultimate stress situation of fetal heartbeat during labour provides a conceptual basis for accommodating heavy crashes. It also suggests a different perspective for evaluating crashes and post crash recovery in order to diagnose, and (ultimately) prognose, ‘economic health’, in addition to monitoring the stock index value.

Beat-ID: Towards a computationally low-cost single heartbeat biometric identity check system based on electrocardiogram wave morphology

PubMed Central

Paiva, Joana S.; Dias, Duarte

2017-01-01

In recent years, safer and more reliable biometric methods have been developed. Apart from the need for enhanced security, the media and entertainment sectors have also been applying biometrics in the emerging market of user-adaptable objects/systems to make these systems more user-friendly. However, the complexity of some state-of-the-art biometric systems (e.g., iris recognition) or their high false rejection rate (e.g., fingerprint recognition) is neither compatible with the simple hardware architecture required by reduced-size devices nor the new trend of implementing smart objects within the dynamic market of the Internet of Things (IoT). It was recently shown that an individual can be recognized by extracting features from their electrocardiogram (ECG). However, most current ECG-based biometric algorithms are computationally demanding and/or rely on relatively large (several seconds) ECG samples, which are incompatible with the aforementioned application fields. Here, we present a computationally low-cost method (patent pending), including simple mathematical operations, for identifying a person using only three ECG morphology-based characteristics from a single heartbeat. The algorithm was trained/tested using ECG signals of different duration from the Physionet database on more than 60 different training/test datasets. The proposed method achieved maximal averaged accuracy of 97.450% in distinguishing each subject from a ten-subject set and false acceptance and rejection rates (FAR and FRR) of 5.710±1.900% and 3.440±1.980%, respectively, placing Beat-ID in a very competitive position in terms of the FRR/FAR among state-of-the-art methods. Furthermore, the proposed method can identify a person using an average of 1.020 heartbeats. It therefore has FRR/FAR behavior similar to obtaining a fingerprint, yet it is simpler and requires less expensive hardware. This method targets low-computational/energy-cost scenarios, such as tiny wearable devices (e.g., a smart object that automatically adapts its configuration to the user). A hardware proof-of-concept implementation is presented as an annex to this paper. PMID:28719614

Beat-ID: Towards a computationally low-cost single heartbeat biometric identity check system based on electrocardiogram wave morphology.

PubMed

Paiva, Joana S; Dias, Duarte; Cunha, João P S

2017-01-01

In recent years, safer and more reliable biometric methods have been developed. Apart from the need for enhanced security, the media and entertainment sectors have also been applying biometrics in the emerging market of user-adaptable objects/systems to make these systems more user-friendly. However, the complexity of some state-of-the-art biometric systems (e.g., iris recognition) or their high false rejection rate (e.g., fingerprint recognition) is neither compatible with the simple hardware architecture required by reduced-size devices nor the new trend of implementing smart objects within the dynamic market of the Internet of Things (IoT). It was recently shown that an individual can be recognized by extracting features from their electrocardiogram (ECG). However, most current ECG-based biometric algorithms are computationally demanding and/or rely on relatively large (several seconds) ECG samples, which are incompatible with the aforementioned application fields. Here, we present a computationally low-cost method (patent pending), including simple mathematical operations, for identifying a person using only three ECG morphology-based characteristics from a single heartbeat. The algorithm was trained/tested using ECG signals of different duration from the Physionet database on more than 60 different training/test datasets. The proposed method achieved maximal averaged accuracy of 97.450% in distinguishing each subject from a ten-subject set and false acceptance and rejection rates (FAR and FRR) of 5.710±1.900% and 3.440±1.980%, respectively, placing Beat-ID in a very competitive position in terms of the FRR/FAR among state-of-the-art methods. Furthermore, the proposed method can identify a person using an average of 1.020 heartbeats. It therefore has FRR/FAR behavior similar to obtaining a fingerprint, yet it is simpler and requires less expensive hardware. This method targets low-computational/energy-cost scenarios, such as tiny wearable devices (e.g., a smart object that automatically adapts its configuration to the user). A hardware proof-of-concept implementation is presented as an annex to this paper.

Heartbeat synchronized with ventilation

NASA Astrophysics Data System (ADS)

Schäfer, Carsten; Rosenblum, Michael G.; Kurths, Jürgen; Abel, Hans-Henning

1998-03-01

It is widely accepted that cardiac and respiratory rhythms in humans are unsynchronised. However, a newly developed data analysis technique allows any interaction that does occur in even weakly coupled complex systems to be observed. Using this technique, we found long periods of hidden cardiorespiratory synchronization, lasting up to 20 minutes, during spontaneous breathing at rest.

Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers

PubMed Central

2014-01-01

Background Left bundle branch block (LBBB) and right bundle branch block (RBBB) not only mask electrocardiogram (ECG) changes that reflect diseases but also indicate important underlying pathology. The timely detection of LBBB and RBBB is critical in the treatment of cardiac diseases. Inter-patient heartbeat classification is based on independent training and testing sets to construct and evaluate a heartbeat classification system. Therefore, a heartbeat classification system with a high performance evaluation possesses a strong predictive capability for unknown data. The aim of this study was to propose a method for inter-patient classification of heartbeats to accurately detect LBBB and RBBB from the normal beat (NORM). Methods This study proposed a heartbeat classification method through a combination of three different types of classifiers: a minimum distance classifier constructed between NORM and LBBB; a weighted linear discriminant classifier between NORM and RBBB based on Bayesian decision making using posterior probabilities; and a linear support vector machine (SVM) between LBBB and RBBB. Each classifier was used with matching features to obtain better classification performance. The final types of the test heartbeats were determined using a majority voting strategy through the combination of class labels from the three classifiers. The optimal parameters for the classifiers were selected using cross-validation on the training set. The effects of different lead configurations on the classification results were assessed, and the performance of these three classifiers was compared for the detection of each pair of heartbeat types. Results The study results showed that a two-lead configuration exhibited better classification results compared with a single-lead configuration. The construction of a classifier with good performance between each pair of heartbeat types significantly improved the heartbeat classification performance. The results showed a sensitivity of 91.4% and a positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a positive predictive value of 88.8% for RBBB. Conclusions A multi-classifier ensemble method was proposed based on inter-patient data and demonstrated a satisfactory classification performance. This approach has the potential for application in clinical practice to distinguish LBBB and RBBB from NORM of unknown patients. PMID:24903422

Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers.

PubMed

Huang, Huifang; Liu, Jie; Zhu, Qiang; Wang, Ruiping; Hu, Guangshu

2014-06-05

Left bundle branch block (LBBB) and right bundle branch block (RBBB) not only mask electrocardiogram (ECG) changes that reflect diseases but also indicate important underlying pathology. The timely detection of LBBB and RBBB is critical in the treatment of cardiac diseases. Inter-patient heartbeat classification is based on independent training and testing sets to construct and evaluate a heartbeat classification system. Therefore, a heartbeat classification system with a high performance evaluation possesses a strong predictive capability for unknown data. The aim of this study was to propose a method for inter-patient classification of heartbeats to accurately detect LBBB and RBBB from the normal beat (NORM). This study proposed a heartbeat classification method through a combination of three different types of classifiers: a minimum distance classifier constructed between NORM and LBBB; a weighted linear discriminant classifier between NORM and RBBB based on Bayesian decision making using posterior probabilities; and a linear support vector machine (SVM) between LBBB and RBBB. Each classifier was used with matching features to obtain better classification performance. The final types of the test heartbeats were determined using a majority voting strategy through the combination of class labels from the three classifiers. The optimal parameters for the classifiers were selected using cross-validation on the training set. The effects of different lead configurations on the classification results were assessed, and the performance of these three classifiers was compared for the detection of each pair of heartbeat types. The study results showed that a two-lead configuration exhibited better classification results compared with a single-lead configuration. The construction of a classifier with good performance between each pair of heartbeat types significantly improved the heartbeat classification performance. The results showed a sensitivity of 91.4% and a positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a positive predictive value of 88.8% for RBBB. A multi-classifier ensemble method was proposed based on inter-patient data and demonstrated a satisfactory classification performance. This approach has the potential for application in clinical practice to distinguish LBBB and RBBB from NORM of unknown patients.

Holter registers and metabolic syndrome

NASA Astrophysics Data System (ADS)

Muñoz-Diosdado, A.; Ramírez-Hernández, L.; Aguilar-Molina, A. M.; Zamora-Justo, J. A.; Gutiérrez-Calleja, R. A.; Virgilio-González, C. D.

2014-11-01

There is a relationship between the state of the cardiovascular system and metabolic syndrome (MS). A way to diagnose the heart state of a person is to monitor the electrical activity of the heart using a 24 hours Holter monitor. Scanned ECG signal can be analyzed beat-by-beat by algorithms that separate normal of abnormal heartbeats. If the percentage of abnormal heartbeats is too high it could be argued that the patient has heart problems. We have algorithms that can not only identify the abnormal heartbeats, but they can also classify them, so we classified and counted abnormal heartbeats in patients with MS and subjects without MS. Most of our patients have large waist circumference, high triglycerides and high levels of LDL (high-density lipoprotein) cholesterol although some of them have high blood pressure. We enrolled adult patients with MS free of diabetes in a four month lifestyle intervention program including diet and physical aerobic exercise, and compared with healthy controls. We made an initial registration with a Holter, and 24 hours ECG signal is analyzed to identify and classify the different types of heartbeats. The patients then begin with diet or exercise (at least half an hour daily). Periodically Holter records were taken up and we describe the evolution in time of the number and type of abnormal heartbeats. Results show that the percentage of abnormal heartbeats decreases over time, in some cases the decline is very significant, and almost a reduction to half or less of abnormal heartbeats after several months since the patients changed their eating or physical activity habits.

Does interoceptive awareness affect the ability to regulate unfair treatment by others?

PubMed

van 't Wout, Mascha; Faught, Sara; Menino, David

2013-01-01

In this study we aimed to investigate how awareness of bodily responses, referred to as interoceptive awareness, influences decision-making in a social interactive context. Interoceptive awareness is thought to be crucial for adequate regulation of one's emotions. However, there is a dearth of studies that examine the association between interoceptive awareness and the ability to regulate emotions during interpersonal decision-making. Here, we quantified interoceptive awareness with a heartbeat detection task in which we measured the difference between subjective self-reports and an objective psychophysiological measurement of participant heart rates. Social decision-making was quantified using a two-round Ultimatum Game. Participants were asked to first reject or accept an unfair division of money proposed by a partner. In turn, participants could then make an offer on how to divide an amount of money with the same partner. Participants performed 20 rounds of the two-round Ultimatum Game twice, once during baseline condition and once while asked to reappraise emotional reactions when confronted with unfair offers from partners. Results showed that after reappraisal participants (1) accepted more unfair offers and (2) offered higher return divisions, as compared to baseline. With respect to interoceptive awareness, participants with better heartbeat detection scores tended to report less emotional involvement when they applied reappraisal while playing the Ultimatum Game. However, there was no reliably significant relationship between heartbeat detection and the acceptance of unfair offers. Similarly, heartbeat detection accuracy was not related to return offers made in the second round of the Ultimatum Game or the habitual use of emotion regulation. These preliminary findings suggest that the relationship between interoceptive awareness and behavioral changes due to emotion regulation in a social decision-making context appears to be complex.

Does interoceptive awareness affect the ability to regulate unfair treatment by others?

PubMed Central

van 't Wout, Mascha; Faught, Sara; Menino, David

2013-01-01

In this study we aimed to investigate how awareness of bodily responses, referred to as interoceptive awareness, influences decision-making in a social interactive context. Interoceptive awareness is thought to be crucial for adequate regulation of one’s emotions. However, there is a dearth of studies that examine the association between interoceptive awareness and the ability to regulate emotions during interpersonal decision-making. Here, we quantified interoceptive awareness with a heartbeat detection task in which we measured the difference between subjective self-reports and an objective psychophysiological measurement of participant heart rates. Social decision-making was quantified using a two-round Ultimatum Game. Participants were asked to first reject or accept an unfair division of money proposed by a partner. In turn, participants could then make an offer on how to divide an amount of money with the same partner. Participants performed 20 rounds of the two-round Ultimatum Game twice, once during baseline condition and once while asked to reappraise emotional reactions when confronted with unfair offers from partners. Results showed that after reappraisal participants (1) accepted more unfair offers and (2) offered higher return divisions, as compared to baseline. With respect to interoceptive awareness, participants with better heartbeat detection scores tended to report less emotional involvement when they applied reappraisal while playing the Ultimatum Game. However, there was no reliably significant relationship between heartbeat detection and the acceptance of unfair offers. Similarly, heartbeat detection accuracy was not related to return offers made in the second round of the Ultimatum Game or the habitual use of emotion regulation. These preliminary findings suggest that the relationship between interoceptive awareness and behavioral changes due to emotion regulation in a social decision-making context appears to be complex. PMID:24348438

Listen to your heart: when false somatic feedback shapes moral behavior.

PubMed

Gu, Jun; Zhong, Chen-Bo; Page-Gould, Elizabeth

2013-05-01

A pounding heart is a common symptom people experience when confronting moral dilemmas. The authors conducted 4 experiments using a false feedback paradigm to explore whether and when listening to a fast (vs. normal) heartbeat sound shaped ethical behavior. Study 1 found that perceived fast heartbeat increased volunteering for a just cause. Study 2 extended this effect to moral transgressions and showed that perceived fast heartbeat reduced lying for self-gain. Studies 3 and 4 explored the boundary conditions of this effect and found that perceived heartbeat had less influence on deception when people are mindful or approach the decision deliberatively. These findings suggest that the perceived physiological experience of fast heartbeats may signal greater distress in moral situations and hence motivate people to take the moral high road. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Patient-Specific Deep Architectural Model for ECG Classification

PubMed Central

Luo, Kan; Cuschieri, Alfred

2017-01-01

Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition. PMID:29065597

HERMA-Heartbeat Microwave Authentication

NASA Technical Reports Server (NTRS)

Haque, Salman-ul Mohammed (Inventor); Chow, Edward (Inventor); McKee, Michael Ray (Inventor); Tkacenko, Andre (Inventor); Lux, James Paul (Inventor)

2018-01-01

Systems and methods for identifying and/or authenticating individuals utilizing microwave sensing modules are disclosed. A HEaRtbeat Microwave Authentication (HERMA) system can enable the active identification and/or authentication of a user by analyzing reflected RF signals that contain a person's unique characteristics related to their heartbeats. An illumination signal is transmitted towards a person where a reflected signal captures the motion of the skin and tissue (i.e. displacement) due to the person's heartbeats. The HERMA system can utilize existing transmitters in a mobile device (e.g. Wi-Fi, Bluetooth, Cellphone signals) as the illumination source with at least one external receive antenna. The received reflected signals can be pre-processed and analyzed to identify and/or authenticate a user.

Heartbeat Stars Artist Concept

NASA Image and Video Library

2016-10-21

This artist's concept depicts "heartbeat stars," which have been detected by NASA's Kepler Space Telescope and others. The illustration shows two heartbeat stars swerving close to one another in their closest approach along their highly elongated orbits around one another. The mutual gravitation of the two stars would cause the stars themselves to become slightly ellipsoidal in shape. A third, more distant star in the system is shown in the upper left. Astronomers speculate that such unseen companions may exist in some of these heartbeat star systems, and could be responsible for maintaining these oddly stretched-out orbits. The overlaid curve depicts the inferred cyclic change in velocities in one such system, called KIC 9965691, looking something like the graph of an electrocardiogram (hence the name "heartbeat stars"). The solid points represent measurements made by the HIRES instrument at the W.M. Keck Observatory, and the curve is the best fit model for the motions of this system. http://photojournal.jpl.nasa.gov/catalog/PIA21075

Heartbeat OCT: in vivo intravascular megahertz-optical coherence tomography

PubMed Central

Wang, Tianshi; Pfeiffer, Tom; Regar, Evelyn; Wieser, Wolfgang; van Beusekom, Heleen; Lancee, Charles T.; Springeling, Geert; Krabbendam, Ilona; van der Steen, Antonius F.W.; Huber, Robert; van Soest, Gijs

2015-01-01

Cardiac motion artifacts, non-uniform rotational distortion and undersampling affect the image quality and the diagnostic impact of intravascular optical coherence tomography (IV-OCT). In this study we demonstrate how these limitations of IV-OCT can be addressed by using an imaging system that we called “Heartbeat OCT”, combining a fast Fourier Domain Mode Locked laser, fast pullback, and a micromotor actuated catheter, designed to examine a coronary vessel in less than one cardiac cycle. We acquired in vivo data sets of two coronary arteries in a porcine heart with both Heartbeat OCT, working at 2.88 MHz A-line rate, 4000 frames/s and 100 mm/s pullback speed, and with a commercial system. The in vivo results show that Heartbeat OCT provides faithfully rendered, motion-artifact free, fully sampled vessel wall architecture, unlike the conventional IV-OCT data. We present the Heartbeat OCT system in full technical detail and discuss the steps needed for clinical translation of the technology. PMID:26713214

Development of unconstrained heartbeat and respiration measurement system with pneumatic flow.

PubMed

Kurihara, Yosuke; Watanabe, Kajiro

2012-12-01

The management of health through daily monitoring of heartbeat and respiration signals is of major importance for early diagnosis to prevent diseases of the respiratory and circulatory system. However, such daily health monitoring is possible only if the monitoring system is physically and psychologically noninvasive. In this paper, an unconstrained method of measuring heartbeat and respiration signals, by using a thermistor to measure the air flows from the air mattress to an air tube accompanying the subject's heartbeat and respiration, is proposed. The SN ratio with interference by opening and closing of a door as environmental noise was compared with that obtained by the conventional condenser microphone method. As a result, the SN ratios with the condenser microphone method were 26.6 ± 4.2 dB for heartbeat and 27.8 ± 3.0 dB for respiration, whereas with the proposed method they were 34.9 ± 3.1 dB and 42.1 ± 2.5 dB, respectively.

Cross-correlation of instantaneous phase increments in pressure-flow fluctuations: Applications to cerebral autoregulation

NASA Astrophysics Data System (ADS)

Chen, Zhi; Hu, Kun; Stanley, H. Eugene; Novak, Vera; Ivanov, Plamen Ch.

2006-03-01

We investigate the relationship between the blood flow velocities (BFV) in the middle cerebral arteries and beat-to-beat blood pressure (BP) recorded from a finger in healthy and post-stroke subjects during the quasisteady state after perturbation for four different physiologic conditions: supine rest, head-up tilt, hyperventilation, and CO2 rebreathing in upright position. To evaluate whether instantaneous BP changes in the steady state are coupled with instantaneous changes in the BFV, we compare dynamical patterns in the instantaneous phases of these signals, obtained from the Hilbert transform, as a function of time. We find that in post-stroke subjects the instantaneous phase increments of BP and BFV exhibit well-pronounced patterns that remain stable in time for all four physiologic conditions, while in healthy subjects these patterns are different, less pronounced, and more variable. We propose an approach based on the cross-correlation of the instantaneous phase increments to quantify the coupling between BP and BFV signals. We find that the maximum correlation strength is different for the two groups and for the different conditions. For healthy subjects the amplitude of the cross-correlation between the instantaneous phase increments of BP and BFV is small and attenuates within 3-5 heartbeats. In contrast, for post-stroke subjects, this amplitude is significantly larger and cross-correlations persist up to 20 heartbeats. Further, we show that the instantaneous phase increments of BP and BFV are cross-correlated even within a single heartbeat cycle. We compare the results of our approach with three complementary methods: direct BP-BFV cross-correlation, transfer function analysis, and phase synchronization analysis. Our findings provide insight into the mechanism of cerebral vascular control in healthy subjects, suggesting that this control mechanism may involve rapid adjustments (within a heartbeat) of the cerebral vessels, so that BFV remains steady in response to changes in peripheral BP.

Cross-correlation of instantaneous phase increments in pressure-flow fluctuations: applications to cerebral autoregulation.

PubMed

Chen, Zhi; Hu, Kun; Stanley, H Eugene; Novak, Vera; Ivanov, Plamen Ch

2006-03-01

We investigate the relationship between the blood flow velocities (BFV) in the middle cerebral arteries and beat-to-beat blood pressure (BP) recorded from a finger in healthy and post-stroke subjects during the quasisteady state after perturbation for four different physiologic conditions: supine rest, head-up tilt, hyperventilation, and CO2 rebreathing in upright position. To evaluate whether instantaneous BP changes in the steady state are coupled with instantaneous changes in the BFV, we compare dynamical patterns in the instantaneous phases of these signals, obtained from the Hilbert transform, as a function of time. We find that in post-stroke subjects the instantaneous phase increments of BP and BFV exhibit well-pronounced patterns that remain stable in time for all four physiologic conditions, while in healthy subjects these patterns are different, less pronounced, and more variable. We propose an approach based on the cross-correlation of the instantaneous phase increments to quantify the coupling between BP and BFV signals. We find that the maximum correlation strength is different for the two groups and for the different conditions. For healthy subjects the amplitude of the cross-correlation between the instantaneous phase increments of BP and BFV is small and attenuates within 3-5 heartbeats. In contrast, for post-stroke subjects, this amplitude is significantly larger and cross-correlations persist up to 20 heartbeats. Further, we show that the instantaneous phase increments of BP and BFV are cross-correlated even within a single heartbeat cycle. We compare the results of our approach with three complementary methods: direct BP-BFV cross-correlation, transfer function analysis, and phase synchronization analysis. Our findings provide insight into the mechanism of cerebral vascular control in healthy subjects, suggesting that this control mechanism may involve rapid adjustments (within a heartbeat) of the cerebral vessels, so that BFV remains steady in response to changes in peripheral BP.

Spectrum analysis of radar life signal in the three kinds of theoretical models

NASA Astrophysics Data System (ADS)

Yang, X. F.; Ma, J. F.; Wang, D.

2017-02-01

In the single frequency continuous wave radar life detection system, based on the Doppler effect, the theory model of radar life signal is expressed by the real function, and there is a phenomenon that can't be confirmed by the experiment. When the phase generated by the distance between the measured object and the radar measuring head is л of integer times, the main frequency spectrum of life signal (respiration and heartbeat) is not existed in radar life signal. If this phase is л/2 of odd times, the main frequency spectrum of breath and heartbeat frequency is the strongest. In this paper, we use the Doppler effect as the basic theory, using three different mathematical expressions——real function, complex exponential function and Bessel's function expansion form. They are used to establish the theoretical model of radar life signal. Simulation analysis revealed that the Bessel expansion form theoretical model solve the problem of real function form. Compared with the theoretical model of the complex exponential function, the derived spectral line is greatly reduced in the theoretical model of Bessel expansion form, which is more consistent with the actual situation.

A deep convolutional neural network model to classify heartbeats.

PubMed

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adam, Muhammad; Gertych, Arkadiusz; Tan, Ru San

2017-10-01

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats. Copyright © 2017 Elsevier Ltd. All rights reserved.

A novel machine learning-enabled framework for instantaneous heart rate monitoring from motion-artifact-corrupted electrocardiogram signals.

PubMed

Zhang, Qingxue; Zhou, Dian; Zeng, Xuan

2016-11-01

This paper proposes a novel machine learning-enabled framework to robustly monitor the instantaneous heart rate (IHR) from wrist-electrocardiography (ECG) signals continuously and heavily corrupted by random motion artifacts in wearable applications. The framework includes two stages, i.e. heartbeat identification and refinement, respectively. In the first stage, an adaptive threshold-based auto-segmentation approach is proposed to select out heartbeat candidates, including the real heartbeats and large amounts of motion-artifact-induced interferential spikes. Then twenty-six features are extracted for each candidate in time, spatial, frequency and statistical domains, and evaluated by a spare support vector machine (SVM) to select out ten critical features which can effectively reveal residual heartbeat information. Afterwards, an SVM model, created on the training data using the selected feature set, is applied to find high confident heartbeats from a large number of candidates in the testing data. In the second stage, the SVM classification results are further refined by two steps: (1) a rule-based classifier with two attributes named 'continuity check' and 'locality check' for outlier (false positives) removal, and (2) a heartbeat interpolation strategy for missing-heartbeat (false negatives) recovery. The framework is evaluated on a wrist-ECG dataset acquired by a semi-customized platform and also a public dataset. When the signal-to-noise ratio is as low as  -7 dB, the mean absolute error of the estimated IHR is 1.4 beats per minute (BPM) and the root mean square error is 6.5 BPM. The proposed framework greatly outperforms well-established approaches, demonstrating that it can effectively identify the heartbeats from ECG signals continuously corrupted by intense motion artifacts and robustly estimate the IHR. This study is expected to contribute to robust long-term wearable IHR monitoring for pervasive heart health and fitness management.

RIGHT AND LEFT VENTRICULAR DIASTOLIC PRESSURE–VOLUME RELATIONS: A COMPREHENSIVE REVIEW

PubMed Central

Pasipoularides, Ares

2012-01-01

Ventricular compliance alterations can affect cardiac performance and adaptations. Moreover, diastolic mechanics are important in assessing both diastolic and systolic function, since any filling impairment can compromise systolic function. A sigmoidal passive filling pressure-volume relationship, developed using chronically instrumented, awake-animal disease models, is clinically adaptable to evaluating diastolic dynamics using subject-specific micromanometric and volumetric data from the entire filling period of any heartbeat(s). This innovative relationship is the global, integrated expression of chamber geometry, wall thickness, and passive myocardial wall properties. Chamber and myocardial compliance curves of both ventricles can be computed by the sigmoidal methodology over the entire filling period and plotted over appropriate filling pressure ranges. Important characteristics of the compliance curves can be examined and compared between the right and the left ventricle, and for different physiological and pathological conditions. The sigmoidal paradigm is more accurate and, therefore, a better alternative to the conventional exponential pressure-volume approximation. PMID:23179133

Revealing Real-Time Emotional Responses: a Personalized Assessment based on Heartbeat Dynamics

NASA Astrophysics Data System (ADS)

Valenza, Gaetano; Citi, Luca; Lanatá, Antonio; Scilingo, Enzo Pasquale; Barbieri, Riccardo

2014-05-01

Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis.

Revealing real-time emotional responses: a personalized assessment based on heartbeat dynamics.

PubMed

Valenza, Gaetano; Citi, Luca; Lanatá, Antonio; Scilingo, Enzo Pasquale; Barbieri, Riccardo

2014-05-21

Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most of the proposed emotion recognition systems require relatively long-time series of multivariate records and do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize the emotional state of a subject through the analysis of heartbeat dynamics exclusively. The study includes thirty subjects presented with a set of standardized images gathered from the international affective picture system, alternating levels of arousal and valence. Due to the intrinsic nonlinearity and nonstationarity of the RR interval series, a specific point-process model was devised for instantaneous identification considering autoregressive nonlinearities up to the third-order according to the Wiener-Volterra representation, thus tracking very fast stimulus-response changes. Features from the instantaneous spectrum and bispectrum, as well as the dominant Lyapunov exponent, were extracted and considered as input features to a support vector machine for classification. Results, estimating emotions each 10 seconds, achieve an overall accuracy in recognizing four emotional states based on the circumplex model of affect of 79.29%, with 79.15% on the valence axis, and 83.55% on the arousal axis.

Dynamic triggering of volcano drumbeat-like seismicity at the Tatun volcano group in Taiwan

NASA Astrophysics Data System (ADS)

Lin, Cheng-Horng

2017-07-01

Periodical seismicity during eruptions has been observed at several volcanoes, such as Mount St. Helens and Soufrière Hills. Movement of magma is often considered one of the most important factors in its generation. Without any magma movement, drumbeat-like (or heartbeat-like) periodical seismicity was detected twice beneath one of the strongest fumarole sites (Dayoukeng) among the Tatun volcano group in northern Taiwan in 2015. Both incidences of drumbeat-like seismicity were respectively started after felt earthquakes in Taiwan, and then persisted for 1-2 d afterward with repetition intervals of ∼18 min between any two adjacent events. The phenomena suggest both drumbeat-like (heartbeat-like) seismicity sequences were likely triggered by dynamic waves generated by the two felt earthquakes. Thus, rather than any involvement of magma, a simplified pumping system within a degassing conduit is proposed to explain the generation of drumbeat-like seismicity. The collapsed rocks within the conduit act as a piston, which was repeatedly lifted up by ascending gas from a deeper reservoir and dropped down when the ascending gas was escaping later. These phenomena show that the degassing process is still very strong in the Tatun volcano group in Taiwan, even though it has been dormant for about several thousand years.

A nonlinear heartbeat dynamics model approach for personalized emotion recognition.

PubMed

Valenza, Gaetano; Citi, Luca; Lanatà, Antonio; Scilingo, Enzo Pasquale; Barbieri, Riccardo

2013-01-01

Emotion recognition based on autonomic nervous system signs is one of the ambitious goals of affective computing. It is well-accepted that standard signal processing techniques require relative long-time series of multivariate records to ensure reliability and robustness of recognition and classification algorithms. In this work, we present a novel methodology able to assess cardiovascular dynamics during short-time (i.e. < 10 seconds) affective stimuli, thus overcoming some of the limitations of current emotion recognition approaches. We developed a personalized, fully parametric probabilistic framework based on point-process theory where heartbeat events are modelled using a 2(nd)-order nonlinear autoregressive integrative structure in order to achieve effective performances in short-time affective assessment. Experimental results show a comprehensive emotional characterization of 4 subjects undergoing a passive affective elicitation using a sequence of standardized images gathered from the international affective picture system. Each picture was identified by the IAPS arousal and valence scores as well as by a self-reported emotional label associating a subjective positive or negative emotion. Results show a clear classification of two defined levels of arousal, valence and self-emotional state using features coming from the instantaneous spectrum and bispectrum of the considered RR intervals, reaching up to 90% recognition accuracy.

Heartbeat detection system using piezoelectric transducer

NASA Astrophysics Data System (ADS)

Hamonangan, Yosua; Purnamaningsih, Wigajatri

2017-02-01

This paper presents a simple piezoelectric based heartbeat detection system. The signal produced by the piezoelectric will undergo signal conditioning and then converted into digital data by Arduino Nano. Using serial communication, the data will be sent to a computer for display and further analysis. The detection of heartbeat is carried out on three locations; wrist, chest, and diaphragm. From the measurement results, it is shown that the system work best when the piezoelectric is placed on wrist.

Synchronization and Cardio-pulmonary feedback in Sleep Apnea

NASA Astrophysics Data System (ADS)

Xu, Limei; Ivanov, Plamen Ch.; Chen, Zhi; Hu, Kun; Paydarfar, David; Stanley, H. Eugene

2004-03-01

Findings indicate a dynamical coupling between respiratory and cardiac function. However, the nature of this nonlinear interaction remains not well understood. We investigate transient patterns in the cardio-pulmonary interaction under healthy conditions by means of cross-correlation and nonlinear synchronization techniques, and we compare how these patterns change under pathologic conditions such as obstructive sleep apnea --- a periodic cessation of breathing during sleep. We find that during apnea episodes the nonlinear features of cardio-pulmonary interaction change intermittently, and can exhibit variations characterized by different time delays in the phase synchronization between breathing and heartbeat dynamics.

Cumulative Effective Hölder Exponent Based Indicator for Real-Time Fetal Heartbeat Analysis during Labour

NASA Astrophysics Data System (ADS)

Struzik, Zbigniew R.; van Wijngaarden, Willem J.

We introduce a special purpose cumulative indicator, capturing in real time the cumulative deviation from the reference level of the exponent h (local roughness, Hölder exponent) of the fetal heartbeat during labour. We verify that the indicator applied to the variability component of the heartbeat coincides with the fetal outcome as determined by blood samples. The variability component is obtained from running real time decomposition of fetal heartbeat into independent components using an adaptation of an oversampled Haar wavelet transform. The particular filters used and resolutions applied are motivated by obstetricial insight/practice. The methodology described has the potential for real-time monitoring of the fetus during labour and for the prediction of the fetal outcome, allerting the attending staff in the case of (threatening) hypoxia.

Physical working principles of medical radar.

PubMed

Aardal, Øyvind; Paichard, Yoann; Brovoll, Sverre; Berger, Tor; Lande, Tor Sverre; Hamran, Svein-Erik

2013-04-01

There has been research interest in using radar for contactless measurements of the human heartbeat for several years. While many systems have been demonstrated, not much attention have been given to the actual physical causes of why this work. The consensus seems to be that the radar senses small body movements correlated with heartbeats, but whether only the movements of the body surface or reflections from internal organs are also monitored have not been answered definitely. There has recently been proposed another theory that blood perfusion in the skin could be the main reason radars are able to detect heartbeats. In this paper, an experimental approach is given to determine the physical causes. The measurement results show that it is the body surface reflections that dominate radar measurements of human heartbeats.

Examining the Influence of Heartbeat on Expert Marksman Performance

DTIC Science & Technology

2016-04-01

factors, such as heartbeat and heart rate, may play a role on a marksman’s shot as well. For instance, work has been done to determine the effect of... heart rate on expert marksmanship. The results of that work show that an expert marksman’s performance did not change in regard to heart rate. (Pojman...marksmanship phase. (The ECG values are not to be confused with the heart rate, which is the frequency of the cardiac cycle.) The heartbeat on an ECG is

Robust detection of heartbeats using association models from blood pressure and EEG signals.

PubMed

Jeon, Taegyun; Yu, Jongmin; Pedrycz, Witold; Jeon, Moongu; Lee, Boreom; Lee, Byeongcheol

2016-01-15

The heartbeat is fundamental cardiac activity which is straightforwardly detected with a variety of measurement techniques for analyzing physiological signals. Unfortunately, unexpected noise or contaminated signals can distort or cut out electrocardiogram (ECG) signals in practice, misleading the heartbeat detectors to report a false heart rate or suspend itself for a considerable length of time in the worst case. To deal with the problem of unreliable heartbeat detection, PhysioNet/CinC suggests a challenge in 2014 for developing robust heart beat detectors using multimodal signals. This article proposes a multimodal data association method that supplements ECG as a primary input signal with blood pressure (BP) and electroencephalogram (EEG) as complementary input signals when input signals are unreliable. If the current signal quality index (SQI) qualifies ECG as a reliable input signal, our method applies QRS detection to ECG and reports heartbeats. Otherwise, the current SQI selects the best supplementary input signal between BP and EEG after evaluating the current SQI of BP. When BP is chosen as a supplementary input signal, our association model between ECG and BP enables us to compute their regular intervals, detect characteristics BP signals, and estimate the locations of the heartbeat. When both ECG and BP are not qualified, our fusion method resorts to the association model between ECG and EEG that allows us to apply an adaptive filter to ECG and EEG, extract the QRS candidates, and report heartbeats. The proposed method achieved an overall score of 86.26 % for the test data when the input signals are unreliable. Our method outperformed the traditional method, which achieved 79.28 % using QRS detector and BP detector from PhysioNet. Our multimodal signal processing method outperforms the conventional unimodal method of taking ECG signals alone for both training and test data sets. To detect the heartbeat robustly, we have proposed a novel multimodal data association method of supplementing ECG with a variety of physiological signals and accounting for the patient-specific lag between different pulsatile signals and ECG. Multimodal signal detectors and data-fusion approaches such as those proposed in this article can reduce false alarms and improve patient monitoring.

Fractal dynamics in physiology: Alterations with disease and aging

PubMed Central

Goldberger, Ary L.; Amaral, Luis A. N.; Hausdorff, Jeffrey M.; Ivanov, Plamen Ch.; Peng, C.-K.; Stanley, H. Eugene

2002-01-01

According to classical concepts of physiologic control, healthy systems are self-regulated to reduce variability and maintain physiologic constancy. Contrary to the predictions of homeostasis, however, the output of a wide variety of systems, such as the normal human heartbeat, fluctuates in a complex manner, even under resting conditions. Scaling techniques adapted from statistical physics reveal the presence of long-range, power-law correlations, as part of multifractal cascades operating over a wide range of time scales. These scaling properties suggest that the nonlinear regulatory systems are operating far from equilibrium, and that maintaining constancy is not the goal of physiologic control. In contrast, for subjects at high risk of sudden death (including those with heart failure), fractal organization, along with certain nonlinear interactions, breaks down. Application of fractal analysis may provide new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as to monitoring the aging process. Similar approaches show promise in assessing other regulatory systems, such as human gait control in health and disease. Elucidating the fractal and nonlinear mechanisms involved in physiologic control and complex signaling networks is emerging as a major challenge in the postgenomic era. PMID:11875196

Endogenous circadian rhythm in human motor activity uncoupled from circadian influences on cardiac dynamics

PubMed Central

Ivanov, Plamen Ch.; Hu, Kun; Hilton, Michael F.; Shea, Steven A.; Stanley, H. Eugene

2007-01-01

The endogenous circadian pacemaker influences key physiologic functions, such as body temperature and heart rate, and is normally synchronized with the sleep/wake cycle. Epidemiological studies demonstrate a 24-h pattern in adverse cardiovascular events with a peak at ≈10 a.m. It is unknown whether this pattern in cardiac risk is caused by a day/night pattern of behaviors, including activity level and/or influences from the internal circadian pacemaker. We recently found that a scaling index of cardiac vulnerability has an endogenous circadian peak at the circadian phase corresponding to ≈10 a.m., which conceivably could contribute to the morning peak in cardiac risk. Here, we test whether this endogenous circadian influence on cardiac dynamics is caused by circadian-mediated changes in motor activity or whether activity and heart rate dynamics are decoupled across the circadian cycle. We analyze high-frequency recordings of motion from young healthy subjects during two complementary protocols that decouple the sleep/wake cycle from the circadian cycle while controlling scheduled behaviors. We find that static activity properties (mean and standard deviation) exhibit significant circadian rhythms with a peak at the circadian phase corresponding to 5–9 p.m. (≈9 h later than the peak in the scale-invariant index of heartbeat fluctuations). In contrast, dynamic characteristics of the temporal scale-invariant organization of activity fluctuations (long-range correlations) do not exhibit a circadian rhythm. These findings suggest that endogenous circadian-mediated activity variations are not responsible for the endogenous circadian rhythm in the scale-invariant structure of heartbeat fluctuations and likely do not contribute to the increase in cardiac risk at ≈10 a.m. PMID:18093917

Endogenous circadian rhythm in human motor activity uncoupled from circadian influences on cardiac dynamics.

PubMed

Ivanov, Plamen Ch; Hu, Kun; Hilton, Michael F; Shea, Steven A; Stanley, H Eugene

2007-12-26

The endogenous circadian pacemaker influences key physiologic functions, such as body temperature and heart rate, and is normally synchronized with the sleep/wake cycle. Epidemiological studies demonstrate a 24-h pattern in adverse cardiovascular events with a peak at approximately 10 a.m. It is unknown whether this pattern in cardiac risk is caused by a day/night pattern of behaviors, including activity level and/or influences from the internal circadian pacemaker. We recently found that a scaling index of cardiac vulnerability has an endogenous circadian peak at the circadian phase corresponding to approximately 10 a.m., which conceivably could contribute to the morning peak in cardiac risk. Here, we test whether this endogenous circadian influence on cardiac dynamics is caused by circadian-mediated changes in motor activity or whether activity and heart rate dynamics are decoupled across the circadian cycle. We analyze high-frequency recordings of motion from young healthy subjects during two complementary protocols that decouple the sleep/wake cycle from the circadian cycle while controlling scheduled behaviors. We find that static activity properties (mean and standard deviation) exhibit significant circadian rhythms with a peak at the circadian phase corresponding to 5-9 p.m. ( approximately 9 h later than the peak in the scale-invariant index of heartbeat fluctuations). In contrast, dynamic characteristics of the temporal scale-invariant organization of activity fluctuations (long-range correlations) do not exhibit a circadian rhythm. These findings suggest that endogenous circadian-mediated activity variations are not responsible for the endogenous circadian rhythm in the scale-invariant structure of heartbeat fluctuations and likely do not contribute to the increase in cardiac risk at approximately 10 a.m.

How the Heart Works

MedlinePlus

... Blood Flow Heartbeat Almost everyone has heard the real or recorded sound of a heartbeat. When your heart beats, it makes a "lub-DUB" sound. Between the time you hear "lub" and "DUB," blood is pumped ...

Fast multi-scale feature fusion for ECG heartbeat classification

NASA Astrophysics Data System (ADS)

Ai, Danni; Yang, Jian; Wang, Zeyu; Fan, Jingfan; Ai, Changbin; Wang, Yongtian

2015-12-01

Electrocardiogram (ECG) is conducted to monitor the electrical activity of the heart by presenting small amplitude and duration signals; as a result, hidden information present in ECG data is difficult to determine. However, this concealed information can be used to detect abnormalities. In our study, a fast feature-fusion method of ECG heartbeat classification based on multi-linear subspace learning is proposed. The method consists of four stages. First, baseline and high frequencies are removed to segment heartbeat. Second, as an extension of wavelets, wavelet-packet decomposition is conducted to extract features. With wavelet-packet decomposition, good time and frequency resolutions can be provided simultaneously. Third, decomposed confidences are arranged as a two-way tensor, in which feature fusion is directly implemented with generalized N dimensional ICA (GND-ICA). In this method, co-relationship among different data information is considered, and disadvantages of dimensionality are prevented; this method can also be used to reduce computing compared with linear subspace-learning methods (PCA). Finally, support vector machine (SVM) is considered as a classifier in heartbeat classification. In this study, ECG records are obtained from the MIT-BIT arrhythmia database. Four main heartbeat classes are used to examine the proposed algorithm. Based on the results of five measurements, sensitivity, positive predictivity, accuracy, average accuracy, and t-test, our conclusion is that a GND-ICA-based strategy can be used to provide enhanced ECG heartbeat classification. Furthermore, large redundant features are eliminated, and classification time is reduced.

Implantable telemetry capsule for monitoring arterial oxygen saturation and heartbeat.

PubMed

Kuwana, K; Dohi, T; Hashimoto, Y; Matsumoto, K; Shimoyama, I

2008-01-01

In this study, we have developed an implantable telemetry capsule for monitoring heartbeat. The capsule has three main functions, monitoring vital signs, transmitting the vital signs, and receiving energy for driving the capsule without wires. We used two wavelengths of LEDs and a photodiode sensitive to the two wavelengths for heartbeat sensor. The arterial oxygen saturation is calculated from the amplitude of the heartbeat signal. We fabricated an FM transmitter whose carrier frequency was 80 MHz. Though the GHz range frequency is generally used in transmission, the attenuation in the human body is large. The size of a common linear antenna is about a quarter of its operating wavelength. We employed a coil-based antenna which can reduce size below the quarter of the wavelength. We fabricated a miniaturized transmitter with the coil-based antenna at lower frequency. Our capsule was driven intermittently. We used a rechargeable battery. When the battery ran down, the battery was charged by wireless using the induced electromotive force. This means that the capsule is capable of monitoring vital signs over the long term. We measured the heartbeat from the middle finger of hand in a water tank as a model of a human body.

Cardiorespiratory system monitoring using a developed acoustic sensor.

PubMed

Abbasi-Kesbi, Reza; Valipour, Atefeh; Imani, Khadije

2018-02-01

This Letter proposes a wireless acoustic sensor for monitoring heartbeat and respiration rate based on phonocardiogram (PCG). The developed sensor comprises a processor, a transceiver which operates at industrial, scientific and medical band and the frequency of 2.54 GHz as well as two capacitor microphones which one for recording the heartbeat and another one for respiration rate. To evaluate the precision of the presented sensor in estimating heartbeat and respiration rate, the sensor is tested on the different volunteers and the obtained results are compared with a gold standard as a reference. The results reveal that root-mean-square error are determined <2.27 beats/min and 0.92 breaths/min for the heartbeat and respiration rate in turn. While the standard deviation of the error is obtained <1.26 and 0.63 for heartbeat and respiration rate, respectively. Also, the sensor estimate sounds of [Formula: see text] to [Formula: see text] obtained PCG signal with sensitivity and specificity 98.1% and 98.3% in turn that make 3% improvement than previous works. The results prove that the sensor can be appropriate candidate for recognising abnormal condition in the cardiorespiratory system.

Vital sign sensing method based on EMD in terahertz band

NASA Astrophysics Data System (ADS)

Xu, Zhengwu; Liu, Tong

2014-12-01

Non-contact respiration and heartbeat rates detection could be applied to find survivors trapped in the disaster or the remote monitoring of the respiration and heartbeat of a patient. This study presents an improved algorithm that extracts the respiration and heartbeat rates of humans by utilizing the terahertz radar, which further lessens the effects of noise, suppresses the cross-term, and enhances the detection accuracy. A human target echo model for the terahertz radar is first presented. Combining the over-sampling method, low-pass filter, and Empirical Mode Decomposition improves the signal-to-noise ratio. The smoothed pseudo Wigner-Ville distribution time-frequency technique and the centroid of the spectrogram are used to estimate the instantaneous velocity of the target's cardiopulmonary motion. The down-sampling method is adopted to prevent serious distortion. Finally, a second time-frequency analysis is applied to the centroid curve to extract the respiration and heartbeat rates of the individual. Simulation results show that compared with the previously presented vital sign sensing method, the improved algorithm enhances the signal-to-noise ratio to 1 dB with a detection accuracy of 80%. The improved algorithm is an effective approach for the detection of respiration and heartbeat signal in a complicated environment.

Automatic zebrafish heartbeat detection and analysis for zebrafish embryos.

PubMed

Pylatiuk, Christian; Sanchez, Daniela; Mikut, Ralf; Alshut, Rüdiger; Reischl, Markus; Hirth, Sofia; Rottbauer, Wolfgang; Just, Steffen

2014-08-01

A fully automatic detection and analysis method of heartbeats in videos of nonfixed and nonanesthetized zebrafish embryos is presented. This method reduces the manual workload and time needed for preparation and imaging of the zebrafish embryos, as well as for evaluating heartbeat parameters such as frequency, beat-to-beat intervals, and arrhythmicity. The method is validated by a comparison of the results from automatic and manual detection of the heart rates of wild-type zebrafish embryos 36-120 h postfertilization and of embryonic hearts with bradycardia and pauses in the cardiac contraction.

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm.

PubMed

Pandit, Diptangshu; Zhang, Li; Liu, Chengyu; Chattopadhyay, Samiran; Aslam, Nauman; Lim, Chee Peng

2017-06-01

Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. The proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation. In comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal. Copyright © 2017 Elsevier B.V. All rights reserved.

Using Continuous Glucose Monitoring Data and Detrended Fluctuation Analysis to Determine Patient Condition

PubMed Central

Thomas, Felicity; Signal, Matthew; Chase, J. Geoffrey

2015-01-01

Patients admitted to critical care often experience dysglycemia and high levels of insulin resistance, various intensive insulin therapy protocols and methods have attempted to safely normalize blood glucose (BG) levels. Continuous glucose monitoring (CGM) devices allow glycemic dynamics to be captured much more frequently (every 2-5 minutes) than traditional measures of blood glucose and have begun to be used in critical care patients and neonates to help monitor dysglycemia. In an attempt to obtain a better insight relating biomedical signals and patient status, some researchers have turned toward advanced time series analysis methods. In particular, Detrended Fluctuation Analysis (DFA) has been a topic of many recent studies in to glycemic dynamics. DFA investigates the “complexity” of a signal, how one point in time changes relative to its neighboring points, and DFA has been applied to signals like the inter-beat-interval of human heartbeat to differentiate healthy and pathological conditions. Analyzing the glucose metabolic system with such signal processing tools as DFA has been enabled by the emergence of high quality CGM devices. However, there are several inconsistencies within the published work applying DFA to CGM signals. Therefore, this article presents a review and a “how-to” tutorial of DFA, and in particular its application to CGM signals to ensure the methods used to determine complexity are used correctly and so that any relationship between complexity and patient outcome is robust. PMID:26134835

Frequency shifting approach towards textual transcription of heartbeat sounds.

PubMed

Arvin, Farshad; Doraisamy, Shyamala; Safar Khorasani, Ehsan

2011-10-04

Auscultation is an approach for diagnosing many cardiovascular problems. Automatic analysis of heartbeat sounds and extraction of its audio features can assist physicians towards diagnosing diseases. Textual transcription allows recording a continuous heart sound stream using a text format which can be stored in very small memory in comparison with other audio formats. In addition, a text-based data allows applying indexing and searching techniques to access to the critical events. Hence, the transcribed heartbeat sounds provides useful information to monitor the behavior of a patient for the long duration of time. This paper proposes a frequency shifting method in order to improve the performance of the transcription. The main objective of this study is to transfer the heartbeat sounds to the music domain. The proposed technique is tested with 100 samples which were recorded from different heart diseases categories. The observed results show that, the proposed shifting method significantly improves the performance of the transcription.

Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics

NASA Technical Reports Server (NTRS)

Iyengar, N.; Peng, C. K.; Morin, R.; Goldberger, A. L.; Lipsitz, L. A.

1996-01-01

We postulated that aging is associated with disruption in the fractallike long-range correlations that characterize healthy sinus rhythm cardiac interval dynamics. Ten young (21-34 yr) and 10 elderly (68-81 yr) rigorously screened healthy subjects underwent 120 min of continuous supine resting electrocardiographic recording. We analyzed the interbeat interval time series using standard time and frequency domain statistics and using a fractal measure, detrended fluctuation analysis, to quantify long-range correlation properties. In healthy young subjects, interbeat intervals demonstrated fractal scaling, with scaling exponents (alpha) from the fluctuation analysis close to a value of 1.0. In the group of healthy elderly subjects, the interbeat interval time series had two scaling regions. Over the short range, interbeat interval fluctuations resembled a random walk process (Brownian noise, alpha = 1.5), whereas over the longer range they resembled white noise (alpha = 0.5). Short (alpha s)- and long-range (alpha 1) scaling exponents were significantly different in the elderly subjects compared with young (alpha s = 1.12 +/- 0.19 vs. 0.90 +/- 0.14, respectively, P = 0.009; alpha 1 = 0.75 +/- 0.17 vs. 0.99 +/- 0.10, respectively, P = 0.002). The crossover behavior from one scaling region to another could be modeled as a first-order autoregressive process, which closely fit the data from four elderly subjects. This implies that a single characteristic time scale may be dominating heartbeat control in these subjects. The age-related loss of fractal organization in heartbeat dynamics may reflect the degradation of integrated physiological regulatory systems and may impair an individual's ability to adapt to stress.

Impaired sympathetic vascular regulation in humans after acute dynamic exercise

NASA Technical Reports Server (NTRS)

Halliwill, J. R.; Taylor, J. A.; Eckberg, D. L.

1996-01-01

1. The reduction in vascular resistance which accompanies acute dynamic exercise does not subside immediately during recovery, resulting in a post-exercise hypotension. This sustained vasodilatation suggests that sympathetic vascular regulation is altered after exercise. 2. Therefore, we assessed the baroreflex control of sympathetic outflow in response to arterial pressure changes, and transduction of sympathetic activity into vascular resistance during a sympatho-excitatory stimulus (isometric handgrip exercise) after either exercise (60 min cycling at 60% peak aerobic power (VO2,peak)) or sham treatment (60 min seated rest) in nine healthy subjects. 3. Both muscle sympathetic nerve activity and calf vascular resistance were reduced after exercise (-29.7 +/- 8.8 and -25.3 +/- 9.1%, both P < 0.05). The baroreflex relation between diastolic pressure and sympathetic outflow was shifted downward after exercise (post-exercise intercept, 218 +/- 38 total integrated activity (heartbeat)-1; post-sham intercept, 318 +/- 51 total integrated activity (heartbeat)-1, P < 0.05), indicating less sympathetic outflow across all diastolic pressures. Further, the relation between sympathetic activity and vascular resistance was attenuated after exercise (post-exercise slope, 0.0031 +/- 0.0007 units (total integrated activity)-1 min; post-sham slope, 0.0100 +/- 0.0033 units (total integrated activity)-1 min, P < 0.05), indicating less vasoconstriction with any increase in sympathetic activity. 4. Thus, both baroreflex control of sympathetic outflow and the transduction of sympathetic activity into vascular resistance are altered after dynamic exercise. We conclude that the vasodilation which underlies post-exercise hypotension results from both neural and vascular phenomena.

Fractal dynamics of heartbeat time series of young persons with metabolic syndrome

NASA Astrophysics Data System (ADS)

Muñoz-Diosdado, A.; Alonso-Martínez, A.; Ramírez-Hernández, L.; Martínez-Hernández, G.

2012-10-01

Many physiological systems have been in recent years quantitatively characterized using fractal analysis. We applied it to study heart variability of young subjects with metabolic syndrome (MS); we examined the RR time series (time between two R waves in ECG) with the detrended fluctuation analysis (DFA) method, the Higuchi's fractal dimension method and the multifractal analysis to detect the possible presence of heart problems. The results show that although the young persons have MS, the majority do not present alterations in the heart dynamics. However, there were cases where the fractal parameter values differed significantly from the healthy people values.

Pore Helices Play a Dynamic Role as Integrators of Domain Motion during Kv11.1 Channel Inactivation Gating*

PubMed Central

Perry, Matthew D.; Ng, Chai Ann; Vandenberg, Jamie I.

2013-01-01

Proteins that form ion-selective pores in the membrane of cells are integral to many rapid signaling processes, including regulating the rhythm of the heartbeat. In potassium channels, the selectivity filter is critical for both endowing an exquisite selectivity for potassium ions, as well as for controlling the flow of ions through the pore. Subtle rearrangements in the complex hydrogen-bond network that link the selectivity filter to the surrounding pore helices differentiate conducting (open) from nonconducting (inactivated) conformations of the channel. Recent studies suggest that beyond the selectivity filter, inactivation involves widespread rearrangements of the channel protein. Here, we use rate equilibrium free energy relationship analysis to probe the structural changes that occur during selectivity filter gating in Kv11.1 channels, at near atomic resolution. We show that the pore helix plays a crucial dynamic role as a bidirectional interface during selectivity filter gating. We also define the molecular bases of the energetic coupling between the pore helix and outer helix of the pore domain that occurs early in the transition from open to inactivated states, as well as the coupling between the pore helix and inner helix late in the transition. Our data demonstrate that the pore helices are more than just static structural elements supporting the integrity of the selectivity filter; instead they play a crucial dynamic role during selectivity filter gating. PMID:23471968

Respiration and heartbeat monitoring using a distributed pulsed MIMO radar.

PubMed

Walterscheid, Ingo; Smith, Graeme E

2017-07-01

This paper addresses non-contact monitoring of physiological signals induced by respiration and heartbeat. To detect the tiny physiological movements of the chest or other parts of the torso, a Mulitple-Input Multiple-Output (MIMO) radar is used. The spatially distributed transmitters and receivers are able to detect the chest surface movements of one or multiple persons in a room. Due to several bistatic measurements at the same time a robust detection and measuring of the breathing and heartbeat rate is possible. Using an appropriate geometrical configuration of the sensors even a localization of the person is feasible.

Changes in the Hurst exponent of heartbeat intervals during physical activity

NASA Astrophysics Data System (ADS)

Martinis, M.; Knežević, A.; Krstačić, G.; Vargović, E.

2004-07-01

The fractal scaling properties of the heartbeat time series are studied in different controlled ergometric regimes using both the improved Hurst rescaled range (R/S) analysis and the detrended fluctuation analysis (DFA). The long-time “memory effect” quantified by the value of the Hurst exponent H>0.5 is found to increase during progressive physical activity in healthy subjects, in contrast to those having stable angina pectoris, where it decreases. The results are also supported by the detrended fluctuation analysis. We argue that this finding may be used as a useful new diagnostic parameter for short heartbeat time series.

Analysis of cardiovascular regulation.

PubMed

Wilhelm, F H; Grossman, P; Roth, W T

1999-01-01

Adequate characterization of hemodynamic and autonomic responses to physical and mental stress can elucidate underlying mechanisms of cardiovascular disease or anxiety disorders. We developed a physiological signal processing system for analysis of continuously recorded ECG, arterial blood pressure (BP), and respiratory signals using the programming language Matlab. Data collection devices are a 16-channel digital, physiological recorder (Vitaport), a finger arterial pressure transducer (Finapres), and a respiratory inductance plethysmograph (Respitrace). Besides the conventional analysis of the physiological channels, power spectral density and transfer functions of respiration, heart rate, and blood pressure variability are used to characterize respiratory sinus arrhythmia (RSA), 0.10-Hz BP oscillatory activity (Mayer-waves), and baroreflex sensitivity. The arterial pressure transducer waveforms permit noninvasive estimation of stroke volume, cardiac output, and systemic vascular resistance. Time trends in spectral composition of indices are assessed using complex demodulation. Transient dynamic changes of cardiovascular parameters at the onset of stress and recovery periods are quantified using a regression breakpoint model that optimizes piecewise linear curve fitting. Approximate entropy (ApEn) is computed to quantify the degree of chaos in heartbeat dynamics. Using our signal processing system we found distinct response patterns in subgroups of patients with coronary artery disease or anxiety disorders, which were related to specific pharmacological and behavioral factors.

Development of Active Rocking Chair with Changing Swing According to Heartbeat Fluctuation

NASA Astrophysics Data System (ADS)

Kawashima, Takeshi

The purpose of this study is to realize a comfortable swing according to the condition of each individual person. A simple active rocking chair is developed, the swing of which changes with the heartbeat fluctuation of the seated person. The active rocking chair is driven by a solenoid for safety and silence. Swinging is strengthened when the period of the heartbeat lengthens, and it is weakened when the period shortens. Swinging is evaluated using a questionnaire of 16 bipolar scales that was developed for the evaluation of comfortable swinging on the basis of the semantic differential (SD) technique. As a result, it is confirmed that comfortable swinging is realized by the developed active rocking chair. Then the acceleration near the ear of the seated person is measured in order to examine the features of comfortable swinging using the feedback of heartbeat fluctuation. It is clarified that the power spectrum of the envelope of the acceleration waveform shows “1/f fluctuation”.

Adaptive spatio-temporal filtering of disturbed ECGs: a multi-channel approach to heartbeat detection in smart clothing.

PubMed

Wiklund, Urban; Karlsson, Marcus; Ostlund, Nils; Berglin, Lena; Lindecrantz, Kaj; Karlsson, Stefan; Sandsjö, Leif

2007-06-01

Intermittent disturbances are common in ECG signals recorded with smart clothing: this is mainly because of displacement of the electrodes over the skin. We evaluated a novel adaptive method for spatio-temporal filtering for heartbeat detection in noisy multi-channel ECGs including short signal interruptions in single channels. Using multi-channel database recordings (12-channel ECGs from 10 healthy subjects), the results showed that multi-channel spatio-temporal filtering outperformed regular independent component analysis. We also recorded seven channels of ECG using a T-shirt with textile electrodes. Ten healthy subjects performed different sequences during a 10-min recording: resting, standing, flexing breast muscles, walking and pushups. Using adaptive multi-channel filtering, the sensitivity and precision was above 97% in nine subjects. Adaptive multi-channel spatio-temporal filtering can be used to detect heartbeats in ECGs with high noise levels. One application is heartbeat detection in noisy ECG recordings obtained by integrated textile electrodes in smart clothing.

Double-path acquisition of pulse wave transit time and heartbeat using self-mixing interferometry

NASA Astrophysics Data System (ADS)

Wei, Yingbin; Huang, Wencai; Wei, Zheng; Zhang, Jie; An, Tong; Wang, Xiulin; Xu, Huizhen

2017-06-01

We present a technique based on self-mixing interferometry for acquiring the pulse wave transit time (PWTT) and heartbeat. A signal processing method based on Continuous Wavelet Transform and Hilbert Transform is applied to extract potentially useful information in the self-mixing interference (SMI) signal, including PWTT and heartbeat. Then, some cardiovascular characteristics of the human body are easily acquired without retrieving the SMI signal by complicated algorithms. Experimentally, the PWTT is measured on the finger and the toe of the human body using double-path self-mixing interferometry. Experimental statistical data show the relation between the PWTT and blood pressure, which can be used to estimate the systolic pressure value by fitting. Moreover, the measured heartbeat shows good agreement with that obtained by a photoplethysmography sensor. The method that we demonstrate, which is based on self-mixing interferometry with significant advantages of simplicity, compactness and non-invasion, effectively illustrates the viability of the SMI technique for measuring other cardiovascular signals.

The Most Massive Heartbeat: Finding the Pulse of ι Orionis

NASA Astrophysics Data System (ADS)

Pablo, Herbert; Richardson, Noel; Fuller, Jim; Moffat, Anthony F. J.; BEST and Ritter Observing Team

2017-11-01

ι Orionis is a massive binary system consisting of O9III + B1 III/IV stars. Though the system has been well studied, much about its fundamental properties have been difficult to determine. In this paper we report on the discovery of the heartbeat phenomenon in ι Orionis making it the most massive heartbeat system currently known. Using this phenomenon we have found empirical values for the masses and radii of both components. Moreover, we report the detection of tidally induced oscillations in an O-type star for the first time. These discoveries open a new avenue for exploring asteroseismology in massive stars.

Inhibition of the spider heartbeat by gravity and vibration

NASA Technical Reports Server (NTRS)

Finck, A.

1984-01-01

The rate and vigor of the spider heartbeat is controlled by an external pacemaker. A mechanical feature of the spider cardio-vascular system is the production of high serum pressure in the prosoma and the legs. This appears to be the source for leg extension. The lyriform organ on the patella of the leg is sensitive to vibratory and kinesthetic stimuli. This sensitivity depends upon the degree of leg extension. Thus the activity of the heart and the response characteristics of the sense receptor are related. The effect of a supra-threshold vibratory or gravitational stimulus is to produce an inhibition and a tachycardia of the spider heartbeat.

Multifractal analysis and the NYHA index

NASA Astrophysics Data System (ADS)

Muñoz-Diosdado, A.; Ramírez-Hernández, L.; Aguilar-Molina, A. M.; Zamora-Justo, J. A.; Gutiérrez-Calleja, R. A.; Virgilio-González, C. D.

2014-11-01

We did multifractal analysis of heartbeat interval time series of healthy persons and patients with congestive heart failure (CHF). To analyze circadian rhythm variations we analyzed time series of 24 hours records and segments of six hours when the subject is asleep and segments of six hours when is awake. A decrease in the multifractality degree occurs in the heartbeat interval time series of CHF patients. This multifractality loss is associated with the width reduction of the spectrum and the complexity loss of the signal. Multifractal spectra of healthy persons are right skewed, but the spectra of CHF patients tend to be symmetrical and in some cases are left skewed. To determine the therapy for CHF patients, cardiologists use an index proposed by the NYHA (New York Heart Association). There is a correlation between this index and the multifractal analysis parameters, i.e. while higher is the NYHA index the width of the multifractal spectrum is lower and it is also more symmetrical. In contrast, patients with NYHA index equal to 1 have multifractal parameters similar to those of healthy subjects.

An ultra low energy biomedical signal processing system operating at near-threshold.

PubMed

Hulzink, J; Konijnenburg, M; Ashouei, M; Breeschoten, A; Berset, T; Huisken, J; Stuyt, J; de Groot, H; Barat, F; David, J; Van Ginderdeuren, J

2011-12-01

This paper presents a voltage-scalable digital signal processing system designed for the use in a wireless sensor node (WSN) for ambulatory monitoring of biomedical signals. To fulfill the requirements of ambulatory monitoring, power consumption, which directly translates to the WSN battery lifetime and size, must be kept as low as possible. The proposed processing platform is an event-driven system with resources to run applications with different degrees of complexity in an energy-aware way. The architecture uses effective system partitioning to enable duty cycling, single instruction multiple data (SIMD) instructions, power gating, voltage scaling, multiple clock domains, multiple voltage domains, and extensive clock gating. It provides an alternative processing platform where the power and performance can be scaled to adapt to the application need. A case study on a continuous wavelet transform (CWT)-based heart-beat detection shows that the platform not only preserves the sensitivity and positive predictivity of the algorithm but also achieves the lowest energy/sample for ElectroCardioGram (ECG) heart-beat detection publicly reported today.

Heartbeat Cycle Length Detection by a Ballistocardiographic Sensor in Atrial Fibrillation and Sinus Rhythm

PubMed Central

Zink, Matthias Daniel; Brüser, Christoph; Winnersbach, Patrick; Napp, Andreas; Leonhardt, Steffen; Marx, Nikolaus; Schauerte, Patrick; Mischke, Karl

2015-01-01

Background. Heart rate monitoring is especially interesting in patients with atrial fibrillation (AF) and is routinely performed by ECG. A ballistocardiography (BCG) foil is an unobtrusive sensor for mechanical vibrations. We tested the correlation of heartbeat cycle length detection by a novel algorithm for a BCG foil to an ECG in AF and sinus rhythm (SR). Methods. In 22 patients we obtained BCG and synchronized ECG recordings before and after cardioversion and examined the correlation between heartbeat characteristics. Results. We analyzed a total of 4317 heartbeats during AF and 2445 during SR with a correlation between ECG and BCG during AF of r = 0.70 (95% CI 0.68–0.71, P < 0.0001) and r = 0.75 (95% CI 0.73–0.77, P < 0.0001) during SR. By adding a quality index, artifacts could be reduced and the correlation increased for AF to 0.76 (95% CI 0.74–0.77, P < 0.0001, n = 3468) and for SR to 0.85 (95% CI 0.83–0.86, P < 0.0001, n = 2176). Conclusion. Heartbeat cycle length measurement by our novel algorithm for BCG foil is feasible during SR and AF, offering new possibilities of unobtrusive heart rate monitoring. This trial is registered with IRB registration number EK205/11. This trial is registered with clinical trials registration number NCT01779674. PMID:26229965

On the pleasantness of a haptic stimulation: how different textures can be recognized through heart rate variability nonlinear analysis.

PubMed

Nardelli, Mimma; Greco, Alberto; Bianchi, Matteo; Scilingo, Enzo Pasquale; Valenza, Gaetano

2016-08-01

The hedonic attributes of cutaneous elicitation play a crucial role in everyday life, influencing our behavior and psychophysical state. However, the correlation between such a hedonic aspect of touch and the Autonomic Nervous System (ANS)-related physiological response, which is intimately connected to emotions, still needs to be deeply investigated. This study reports on caress-like stimuli conveyed to the forearm of 32 healthy subjects through different fabrics actuated by a haptic device, which can control both the strength (i.e. the normal force exerted by the fabric) and the velocity of the elicitation. The mimicked caresses were elicited with a fixed force of 6 N, two levels of velocity of 9.4 mm/sec and 65 mm/sec, and four different fabrics with different textures: burlap, hemp, velvet and silk. Participants were asked to score the caress-like stimuli in terms of arousal and valence through a self-assessment questionnaire. Heartbeat data related to the perceived most pleasant (silk) and unpleasant (burlap) fabrics were used as an input to an automatic pattern recognition procedure. Accordingly, considering gender differences, support vector machines using features extracted from linear and nonlinear heartbeat dynamics showed a recognition accuracy of 84.38% (men) and 78.13% (women) while discerning between burlap and silk elicitations at the higher velocity. Results suggest that the fabrics used for the caress-like stimulation significantly affect the nonlinear cardiovascular dynamics, also showing differences according to gender.

Complexity of the heart rhythm after heart transplantation by entropy of transition network for RR-increments of RR time intervals between heartbeats.

PubMed

Makowiec, Danuta; Struzik, Zbigniew; Graff, Beata; Wdowczyk-Szulc, Joanna; Zarczynska-Buchnowiecka, Marta; Gruchala, Marcin; Rynkiewicz, Andrzej

2013-01-01

Network models have been used to capture, represent and analyse characteristics of living organisms and general properties of complex systems. The use of network representations in the characterization of time series complexity is a relatively new but quickly developing branch of time series analysis. In particular, beat-to-beat heart rate variability can be mapped out in a network of RR-increments, which is a directed and weighted graph with vertices representing RR-increments and the edges of which correspond to subsequent increments. We evaluate entropy measures selected from these network representations in records of healthy subjects and heart transplant patients, and provide an interpretation of the results.

Investigation of synchronization between musical beat and heartbeat with cardio-music synchrogram

NASA Astrophysics Data System (ADS)

Fukumoto, Makoto; Nomura, Shusaku; Sawai, Masahiro; Imai, Jun-Ichi; Nagashima, Tomomasa

To illuminate the synchronization phenomena between heartbeat and music, the effects of a sedative music of variable tempo on heart rates were investigated. In the experiment, nine subjects were exposed to the sedative music with having changes in its tempo. The tempo gradually increases, decreases, or stands stable in the music (hereafter these experimental condition are named as Up, Down, and Flat condition). With regard to the analysis of synchronization, we introduced our formerly developed Cardio-Music Synchrogram, which was used to extract statistically significant synchronization period between heartbeat and music. As a result, it was suggested that the sedative music in Down condition induced synchronization more frequently than Flat and Up conditions.

Multiresolution Wavelet Analysis of Heartbeat Intervals Discriminates Healthy Patients from Those with Cardiac Pathology

NASA Astrophysics Data System (ADS)

Thurner, Stefan; Feurstein, Markus C.; Teich, Malvin C.

1998-02-01

We applied multiresolution wavelet analysis to the sequence of times between human heartbeats ( R-R intervals) and have found a scale window, between 16 and 32 heartbeat intervals, over which the widths of the R-R wavelet coefficients fall into disjoint sets for normal and heart-failure patients. This has enabled us to correctly classify every patient in a standard data set as belonging either to the heart-failure or normal group with 100% accuracy, thereby providing a clinically significant measure of the presence of heart failure from the R-R intervals alone. Comparison is made with previous approaches, which have provided only statistically significant measures.

Feeling Anxious: A Twin Study of Panic/Somatic Ratings, Anxiety Sensitivity and Heartbeat Perception in Children

ERIC Educational Resources Information Center

Eley, Thalia C.; Gregory, Alice M.; Clark, David M.; Ehlers, Anke

2007-01-01

Background: Little is known about mechanisms of genetic influence on panic, particularly in childhood. Cognitive theories of panic disorder highlight threatening interpretations of physical sensations, and increased awareness of such sensations. Specifically, anxiety sensitivity (AS) and heartbeat perception (HBP) have been associated with panic…

Fiber-optic couplers as displacement sensors

NASA Astrophysics Data System (ADS)

Baruch, Martin C.; Gerdt, David W.; Adkins, Charles M.

2003-04-01

We introduce the novel concept of using a fiber-optic coupler as a versatile displacement sensor. Comparatively long fiber-optic couplers, with a coupling region of approximately 10 mm, are manufactured using standard communication SM fiber and placed in a looped-back configuration. The result is a displacement sensor, which is robust and highly sensitive over a wide dynamic range. This displacement sensor resolves 1-2 μm over distances of 1-1.5 mm and is characterized by the essential absence of a 'spring constant' plaguing other strain gauge-type sensors. Consequently, it is possible to couple to extremely weak vibrations, such as the skin displacement affected by arterial heart beat pulsations. Used as a wrist-worn heartbeat monitor, the fidelity of the arterial pulse signal has been shown to be so high that it is possible to not only determine heartbeat and breathing rates, but to implement a new single-point blood pressure measurement scheme which does not squeeze the arm. In an application as a floor vibration sensor for the non-intrusive monitoring of independently living elderly, the sensor has been shown to resolve the distinct vibration spectra of different persons and different events.

Using cloud models of heartbeats as the entity identifier to secure mobile devices.

PubMed

Fu, Donglai; Liu, Yanhua

2017-01-01

Mobile devices are extensively used to store more private and often sensitive information. Therefore, it is important to protect them against unauthorised access. Authentication ensures that authorised users can use mobile devices. However, traditional authentication methods, such as numerical or graphic passwords, are vulnerable to passive attacks. For example, an adversary can steal the password by snooping from a shorter distance. To avoid these problems, this study presents a biometric approach that uses cloud models of heartbeats as the entity identifier to secure mobile devices. Here, it is identified that these concepts including cloud model or cloud have nothing to do with cloud computing. The cloud model appearing in the study is the cognitive model. In the proposed method, heartbeats are collected by two ECG electrodes that are connected to one mobile device. The backward normal cloud generator is used to generate ECG standard cloud models characterising the heartbeat template. When a user tries to have access to their mobile device, cloud models regenerated by fresh heartbeats will be compared with ECG standard cloud models to determine if the current user can use this mobile device. This authentication method was evaluated from three aspects including accuracy, authentication time and energy consumption. The proposed method gives 86.04% of true acceptance rate with 2.73% of false acceptance rate. One authentication can be done in 6s, and this processing consumes about 2000 mW of power.

A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals

PubMed Central

2014-01-01

Background The inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB). Methods Based on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated. Results Results showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast. Conclusions A hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in clinical practice. PMID:24981916

Listen to Your Heart: When False Somatic Feedback Shapes Moral Behavior

ERIC Educational Resources Information Center

Gu, Jun; Zhong, Chen-Bo; Page-Gould, Elizabeth

2013-01-01

A pounding heart is a common symptom people experience when confronting moral dilemmas. The authors conducted 4 experiments using a false feedback paradigm to explore whether and when listening to a fast (vs. normal) heartbeat sound shaped ethical behavior. Study 1 found that perceived fast heartbeat increased volunteering for a just cause. Study…

Interoception across Modalities: On the Relationship between Cardiac Awareness and the Sensitivity for Gastric Functions

PubMed Central

Herbert, Beate M.; Muth, Eric R.; Pollatos, Olga; Herbert, Cornelia

2012-01-01

The individual sensitivity for ones internal bodily signals (“interoceptive awareness”) has been shown to be of relevance for a broad range of cognitive and affective functions. Interoceptive awareness has been primarily assessed via measuring the sensitivity for ones cardiac signals (“cardiac awareness”) which can be non-invasively measured by heartbeat perception tasks. It is an open question whether cardiac awareness is related to the sensitivity for other bodily, visceral functions. This study investigated the relationship between cardiac awareness and the sensitivity for gastric functions in healthy female persons by using non-invasive methods. Heartbeat perception as a measure for cardiac awareness was assessed by a heartbeat tracking task and gastric sensitivity was assessed by a water load test. Gastric myoelectrical activity was measured by electrogastrography (EGG) and subjective feelings of fullness, valence, arousal and nausea were assessed. The results show that cardiac awareness was inversely correlated with ingested water volume and with normogastric activity after water load. However, persons with good and poor cardiac awareness did not differ in their subjective ratings of fullness, nausea and affective feelings after drinking. This suggests that good heartbeat perceivers ingested less water because they subjectively felt more intense signals of fullness during this lower amount of water intake compared to poor heartbeat perceivers who ingested more water until feeling the same signs of fullness. These findings demonstrate that cardiac awareness is related to greater sensitivity for gastric functions, suggesting that there is a general sensitivity for interoceptive processes across the gastric and cardiac modality. PMID:22606278

Early classification of pathological heartbeats on wireless body sensor nodes.

PubMed

Braojos, Rubén; Beretta, Ivan; Ansaloni, Giovanni; Atienza, David

2014-11-27

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject's bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections.

Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes

PubMed Central

Braojos, Rubén; Beretta, Ivan; Ansaloni, Giovanni; Atienza, David

2014-01-01

Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject's bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections. PMID:25436654

Aroused with heart: Modulation of heartbeat evoked potential by arousal induction and its oscillatory correlates

PubMed Central

Luft, Caroline Di Bernardi; Bhattacharya, Joydeep

2015-01-01

Recent studies showed that the visceral information is constantly processed by the brain, thereby potentially influencing cognition. One index of such process is the heartbeat evoked potential (HEP), an ERP component related to the cortical processing of the heartbeat. The HEP is sensitive to a number of factors such as motivation, attention, pain, which are associated with higher levels of arousal. However, the role of arousal and its associated brain oscillations on the HEP has not been characterized, yet it could underlie the previous findings. Here we analysed the effects of high- (HA) and low-arousal (LA) induction on the HEP. Further, we investigated the brain oscillations and their role in the HEP in response to HA and LA inductions. As compared to LA, HA was associated with a higher HEP and lower alpha oscillations. Interestingly, individual differences in the HEP modulation by arousal induction were correlated with alpha oscillations. In particular, participants with higher alpha power during the arousal inductions showed a larger HEP in response to HA compared to LA. In summary, we demonstrated that arousal induction affects the cortical processing of heartbeats; and that the alpha oscillations may modulate this effect. PMID:26503014

My heart is in my hands: the interoceptive nature of the spontaneous sensations felt on the hands.

PubMed

Michael, George A; Naveteur, Janick; Dupuy, Marie-Agnès; Jacquot, Laurence

2015-05-01

Somatic sensations may arise in the total absence of external stimuli, i.e., spontaneous sensations (SPSs). Because the background of body sensations has been mentioned as a possible contributor to interoceptive functions, such as the perception of the self and the conscious awareness of one's own body, a possible link between SPSs and interoception has been advocated. Yet, no study has provided direct evidence on such a relationship. The aim of the present study was to establish a link between SPSs and interoception. On the basis of the literature, the accuracy of heartbeat perception was taken as an index of general interoception across different bodily modalities. It was found that individuals with good heartbeat perception experienced more numerous and more intense SPSs. Furthermore, taken along with other individual characteristics, heartbeat perception accuracy predicted the perceived intensity of SPSs, their spatial extent, their variety, as well as confidence in their spatial characteristics. However, we also provide evidence that good vs. poor heartbeat perception is not just a matter of degree. We conclude that interoception definitely contributes to the perception of SPSs. Copyright © 2015 Elsevier Inc. All rights reserved.

Body conscious? Interoceptive awareness, measured by heartbeat perception, is negatively correlated with self-objectification.

PubMed

Ainley, Vivien; Tsakiris, Manos

2013-01-01

'Self-objectification' is the tendency to experience one's body principally as an object, to be evaluated for its appearance rather than for its effectiveness. Within objectification theory, it has been proposed that self-objectification accounts for the poorer interoceptive awareness observed in women, as measured by heartbeat perception. Our study is, we believe, the first specifically to test this relationship. Using a well-validated and reliable heartbeat perception task, we measured interoceptive awareness in women and compared this with their scores on the Self-Objectification Questionnaire, the Self-Consciousness Scale and the Body Consciousness Questionnaire. Interoceptive awareness was negatively correlated with self-objectification. Interoceptive awareness, public body consciousness and private body consciousness together explained 31% of the variance in self-objectification. However, private body consciousness was not significantly correlated with interoceptive awareness, which may explain the many nonsignificant results in self-objectification studies that have used private body consciousness as a measure of body awareness. We propose interoceptive awareness, assessed by heartbeat perception, as a measure of body awareness in self-objectification studies. Our findings have implications for those clinical conditions, in women, which are characterised by self-objectification and low interoceptive awareness, such as eating disorders.

Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.

PubMed

Osowski, Stanislaw; Siwek, Krzysztof; Siroic, Robert

2011-03-01

This paper presents the application of genetic algorithm for the integration of neural classifiers combined in the ensemble for the accurate recognition of heartbeat types on the basis of ECG registration. The idea presented in this paper is that using many classifiers arranged in the form of ensemble leads to the increased accuracy of the recognition. In such ensemble the important problem is the integration of all classifiers into one effective classification system. This paper proposes the use of genetic algorithm. It was shown that application of the genetic algorithm is very efficient and allows to reduce significantly the total error of heartbeat recognition. This was confirmed by the numerical experiments performed on the MIT BIH Arrhythmia Database. Copyright © 2011 Elsevier Ltd. All rights reserved.

Heartbeat of the Southern Oscillation explains ENSO climatic resonances

NASA Astrophysics Data System (ADS)

Bruun, John T.; Allen, J. Icarus; Smyth, Timothy J.

2017-08-01

The El Niño-Southern Oscillation (ENSO) nonlinear oscillator phenomenon has a far reaching influence on the climate and human activities. The up to 10 year quasi-period cycle of the El Niño and subsequent La Niña is known to be dominated in the tropics by nonlinear physical interaction of wind with the equatorial waveguide in the Pacific. Long-term cyclic phenomena do not feature in the current theory of the ENSO process. We update the theory by assessing low (>10 years) and high (<10 years) frequency coupling using evidence across tropical, extratropical, and Pacific basin scales. We analyze observations and model simulations with a highly accurate method called Dominant Frequency State Analysis (DFSA) to provide evidence of stable ENSO features. The observational data sets of the Southern Oscillation Index (SOI), North Pacific Index Anomaly, and ENSO Sea Surface Temperature Anomaly, as well as a theoretical model all confirm the existence of long-term and short-term climatic cycles of the ENSO process with resonance frequencies of {2.5, 3.8, 5, 12-14, 61-75, 180} years. This fundamental result shows long-term and short-term signal coupling with mode locking across the dominant ENSO dynamics. These dominant oscillation frequency dynamics, defined as ENSO frequency states, contain a stable attractor with three frequencies in resonance allowing us to coin the term Heartbeat of the Southern Oscillation due to its characteristic shape. We predict future ENSO states based on a stable hysteresis scenario of short-term and long-term ENSO oscillations over the next century.