Sorting ECGs by lag irreversibility

TL;DR

This paper introduces the lag irreversibility function to measure time-irreversibility in discrete time series and applies it to ECG data, effectively distinguishing between healthy and diseased groups with high accuracy.

Contribution

The paper presents a novel lag irreversibility method for analyzing time-irreversibility in signals, demonstrated on ECG data for disease discrimination.

Findings

Effective discrimination between healthy and diseased ECG groups

High accuracy in disease classification using ROC analysis

Method captures variability in ECG wave amplitudes

Abstract

In this work we introduce the lag irreversibility function as a method to assess time-irreversibility in discrete time series. It quantifies the degree of time-asymmetry for the joint probability function of the state variable under study and the state variable lagged in time. We test its performance in a time-irreversible Markov chain model for which theoretical results are known. Moreover, we use our approach to analyze electrocardiographic recordings of four groups of subjects: healthy young individuals, healthy elderly individuals, and persons with two different disease conditions, namely, congestive heart failure and atrial fibrillation. We find that by studying jointly the variability of the amplitudes of the different waves in the electrocardiographic signals, one can obtain an efficient method to discriminate between the groups already mentioned. Finally, we test the accuracy of our method using the ROC analysis.