Abstract
Detecting cardiac disorders from multi-channel ECG has significant implications for cardiac care. Current methods face challenges due to ECG waveform variations by electrode placement, high signal non-linearity, and low millivolt amplitudes. The present study introduces a non-linear analysis approach leveraging Recurrence plot visualizations as the patterned occurrence of well-defined structures, such as the QRS complex, can be exploited effectively using Recurrence plots. Using the Physikalisch-Technische Bundesanstalt dataset from PhysioNet, we examined four cardiac disorder classes- Myocardial infarction, Bundle branch blocks, Cardiomyopathy, Dysrhythmia, and healthy controls, achieving an impressive classification accuracy of 100%. Wilcoxon rank-sum test is performed at 95% C.I. on Recurrence Quantitative Analysis (RQA) features, identifying five features with statistically significant differences across pairs of study groups. Additionally, t-SNE visualizations of latent space embeddings derived from Recurrence plots and RQA features reveal clear separation among cardiac disorders and healthy subjects, underscoring the efficacy of the proposed approach.