PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gaussian mixture model of heart rate variability
Tommaso Costa, Giuseppe Boccignone and Mario Ferraro
PLoS ONE Volume 7, Number 5, 2012. ISSN 1932-6203

Abstract

Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Brain Computer Interfaces
Multimodal Integration
ID Code:9611
Deposited By:Giuseppe Boccignone
Deposited On:01 December 2012