Why the study?
Convolutional Neural Network models for automated ECG classification are widely used but often overlook structural relationships between leads.
Does a graph-based feature selection approach leveraging inter-lead relationships improve ECG classification accuracy using CNN models?
Does a graph-based feature selection approach leveraging inter-lead relationships improve ECG classification accuracy using CNN models?
A graph-based feature selection approach that captures structural dependencies between ECG leads achieves high accuracy in both binary and multi-class cardiac condition classification using CNNs.
The precise identification of ECG patterns serves as an essential requirement for developing automated cardiac diagnosis systems. While Convolutional Neural Network (CNN) models are widely used, they often overlook inter-lead relationships. This research introduced a graph-based feature selection approach that depends on ECG-lead structural relationships to enhance diagnostic precision. Rather than treating all the leads equally, an adjacency matrix is formed based on lead correlations. The approach captures inter-lead relationships, and features are extracted from the upper triangle of the matrix to prioritize significant lead interactions. The methodology selects important lead interactions so that only the most relevant features influence the classification. Two Convolutional Neural Network (CNN) models are proposed: a binary classifier distinguishing normal from abnormal ECGs, achieving 97.61% accuracy, and a multi-class classifier identifying eight specific cardiac conditions, with 9 1. 7 8 % accuracy. These results demonstrate the effectiveness of the proposed approach in improving ECG classification by prioritizing meaningful lead interactions.
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Benaichouche et al. (2025) studied this question.
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