Key result
Novel amplitude-preserving normalization and recurrence network topology achieves ~94% accuracy classifying normal and abnormal ECGs.
Why the study?
Does a recurrence network-based approach with amplitude-preserving normalization improve the classification accuracy of normal vs abnormal ECG signals?
Does a recurrence network-based approach with amplitude-preserving normalization improve the classification accuracy of normal vs abnormal ECG signals?
A novel recurrence network-based approach with amplitude-preserving normalization provides high accuracy for automated ECG classification using a single lead.
Supports automated single-lead ECG screening in practice; extends recurrence network methods to cardiovascular diagnostics.
This study proposes a novel normalization method that retains signal-specific amplitude characteristics, improving the differentiation between healthy and unhealthy electrocardiogram (ECG) signals. We present a nonlinear network-based approach to analyze ECG dynamics using a recurrence network (RN), described by characteristic path length, link density, and the clustering coefficient as key network measures. To quantify signal complexity, we introduce weighted Shannon entropy measures based on the distributions of shortest path lengths and clustering coefficients in the RN. Degree heterogeneity is further investigated to examine network-level local node variability between cardiac conditions. A comprehensive analysis using the Physikalisch-Technische Bundesanstalt (PTB) Diagnostic ECG Database of Physionet shows that the proposed approach effectively differentiates between normal and abnormal signals, including bundle branch block, myocardial infarction, dysrhythmias, hypertrophy, and cardiomyopathy even when only the V1 lead is used. The method achieves a classification accuracy of 93.5% for Random Forest and 91.7% with XGBoost, confirming the robustness of recurrence-based features in short-duration ECG analysis. The recurrence-based topological and complexity measures, integrated with the proposed amplitude-preserving normalization, quantitatively agree with physiological mechanisms such as conduction delay, repolarization instability, and morphological irregularity, offering a reliable framework for real-time and clinically useful cardiac diagnosis.
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L et al. (2025) studied Abnormal ECG (bundle branch block, myocardial infarction, dysrhythmias, hypertrophy, cardiomyopathy). Recurrence network topology with amplitude-preserving normalization was evaluated on Classification accuracy. A novel amplitude-preserving normalization and recurrence network topology method achieved a classification accuracy of 93.5% using Random Forest for differentiating normal and abnormal ECG signals.
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