Multiplex Recurrence Networks from multi-lead ECG data

TL;DR

This paper introduces a multiplex recurrence network framework for analyzing multi-lead ECG data, revealing disease-specific patterns and differences in recurrence dynamics across healthy and diseased states.

Contribution

The study develops a novel multiplex recurrence network approach to analyze multi-channel ECG data, capturing subtle spatio-temporal variations and disease-related differences.

Findings

Healthy ECGs show higher coherence and mutual information in MRNs.

Diseased ECGs exhibit significant differences in layer similarity measures.

Localized abnormalities like bundle branch block affect coherence measures most.

Abstract

We present an integrated approach to analyse the multi-lead ECG data using the frame work of multiplex recurrence networks (MRNs). We explore how their intralayer and interlayer topological features can capture the subtle variations in the recurrence patterns of the underlying spatio-temporal dynamics. We find MRNs from ECG data of healthy cases are significantly more coherent with high mutual information and less divergence between respective degree distributions. In cases of diseases, significant differences in specific measures of similarity between layers are seen. The coherence is affected most in the cases of diseases associated with localized abnormality such as bundle branch block. We note that it is important to do a comprehensive analysis using all the measures to arrive at disease-specific patterns. Our approach is very general and as such can be applied in any other domain where multivariate or multi-channel data are available from highly complex systems.