A Lightweight CBAM-ECGnet for Explainable Arrhythmia Classification

Cardiac arrhythmia is a major cause of cardiovascular mortality, therefore accurate and timely analysis of ECG is essential for early diagnosis. In this study, a lightweight One-dimensional Convolutional Neural Network (1D CNN) with Convolutional Block Attention Module (CBAM) is proposed for explainable ECG classification according to the AAMI standard. The CBAM-ECGnet model performs convolutional feature extraction with channel and spatial attention with only 4,088 total parameters (≈16 KB). The proposed model is evaluated on the MIT-BIH Arrhythmia Database and achieves an overall accuracy of 98.53%, and a ROC-AUC of 99% . In this study we use Grad-CAM to visualize the regions of the ECG that contribute to each prediction, improving the explainability of the model. This model achieves high accuracy classification with low computational complexity and thus is suitable for real-time and low-resource ECG monitoring application.