Discrimination and relevance determination of heart rate variability features for the identification of congestive heart failure

Abstract

We propose a machine learning framework that implements automated relevance determination in order to identify the deciding RR interval features for the discrimination between congestive heart failure and healthy condition. As a result, the most relevant features of heart rate variability (HRV) are narrowly located spectral components in the very-low and low frequency band, and specific ordinal patterns. HRV is generally reduced in comparison to the healthy condition; also the autonomic regulation of heart rate acceleration and deceleration appears to be pathlogically inversed.