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

Author

Quer, Giorgio ; Rao, Ramesh R.

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of California, La Jolla, La Jolla, CA, USA

fYear

2012

fDate

10-13 Oct. 2012

Firstpage

223

Lastpage

229

Abstract

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.

Keywords

Bayes methods; cardiology; diseases; medical signal processing; neurophysiology; physiological models; statistical distributions; Bayesian model; autonomic nervous system; disease conditions; health conditions; instantaneous heart rate signal; parasympathetic divisions; probabilistic distribution; real-time physiological information; Bayesian methods;

fLanguage

English

Publisher

ieee

Conference_Titel

e-Health Networking, Applications and Services (Healthcom), 2012 IEEE 14th International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4577-2039-0

Electronic_ISBN

978-1-4577-2038-3

Type

conf

DOI

10.1109/HealthCom.2012.6379412

Filename

6379412