Exploring Complexity Changes in Diseased ECG Signals for Enhanced Classification
This work addresses improved ECG classification for medical diagnosis, but it is incremental as it builds on existing methods with modest gains.
The study tackled the problem of classifying diseased ECG signals by analyzing complexity changes, finding significant differences between healthy and diseased classes and improving classification accuracy from an AUC of 0.86 to 0.90 with nonlinear and cross-time series metrics.
The complex dynamics of the heart are reflected in its electrical activity, captured through electrocardiograms (ECGs). In this study we use nonlinear time series analysis to understand how ECG complexity varies with cardiac pathology. Using the large PTB-XL dataset, we extracted nonlinear measures from lead II ECGs, and cross-channel metrics (leads II, V2, AVL) using Spearman correlations and mutual information. Significant differences between diseased and healthy individuals were found in almost all measures between healthy and diseased classes, and between 5 diagnostic superclasses ($p<.001$). Moreover, incorporating these complexity quantifiers into machine learning models substantially improved classification accuracy measured using area under the ROC curve (AUC) from 0.86 (baseline) to 0.87 (nonlinear measures) and 0.90 (including cross-time series metrics).