A Bayesian model of heart rate to reveal real-time physiological information

The human heart rate is influenced by different internal systems of the body and can reveal valuable information about health and disease conditions. In this paper, we analyze the instantaneous heart rate signal using a Bayesian method, inferring in real time a probabilistic distribution that approximates the real distribution of this signal. The best model is chosen after an experimental analysis of real data collected within our framework. The parameters of this distribution can reveal interesting insights on the influences of the sympathetic and parasympathetic divisions of the autonomic nervous system (ANS) in real time.

[1]  Kelvin E. Jones,et al.  Heart rate variability and muscle sympathetic nerve activity response to acute stress: the effect of breathing. , 2010, American journal of physiology. Regulatory, integrative and comparative physiology.

[2]  A L Goldberger,et al.  The pNNx files: re-examining a widely used heart rate variability measure , 2002, Heart.

[3]  Ramesh R. Rao,et al.  Wavelet coherence reveals entrainment of heart rate variability among people involved in group activities , 2012, 2012 IEEE International Conference on Communications (ICC).

[4]  J. LaFountain.  Inc. , 2013, American Art.

[5]  Michele Zorzi,et al.  Sensing, Compression, and Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework , 2012, IEEE Transactions on Wireless Communications.

[6]  David Shannahoff-Khalsa,et al.  Kundalini Yoga Meditation: Techniques Specific for Psychiatric Disorders, Couples Therapy, and Personal Growth , 2006 .

[7]  E. Brown,et al.  A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability. , 2005, American journal of physiology. Heart and circulatory physiology.

[8]  Phyllis K. Stein,et al.  Clinical Application of Heart Rate Variability after Acute Myocardial Infarction , 2012, Front. Physio..

[9]  M. Malik,et al.  Deceleration capacity of heart rate as a predictor of mortality after myocardial infarction: cohort study , 2006, The Lancet.

[10]  K. Tracey.  Physiology and immunology of the cholinergic antiinflammatory pathway. , 2007, The Journal of clinical investigation.

[11]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[12]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[13]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[14]  M. Malik,et al.  Phase-rectified signal averaging detects quasi-periodicities in non-stationary data , 2006 .

[15]  Andrew T. Walden,et al.  A Statistical Study of Temporally Smoothed Wavelet Coherence , 2010, IEEE Transactions on Signal Processing.

[16]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[17]  Kameshwar Poolla,et al.  Threshold modeling of autonomic control of heart rate variability , 2000, IEEE Transactions on Biomedical Engineering.