CBAM-CNN-BiLSTM: A Dual-Attention Deep Learning Architecture for Explainable ECG Arrhythmia Classification and Cardiac Disease Prediction

Registration ID: IJNRD_325697

Published ID: IJNRD2605819

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Prathibha A, Dr. Latha P H, Sandeep Shivashettar

ECG arrhythmia classification, CBAM attention, dilated convolutional network, BiLSTM, explainable AI, Grad-CAM, SHAP, Monte Carlo Dropout, AAMI EC57, MIT-BIH, cardiac disease prediction, signal quality index, clinical deployment

Interpretation of ECG signals is an ongoing problem in medical cardiology, especially regarding the rare arrhythmia classification with respect to class imbalance and high complexity of arrhythmia morphology. This paper presents CBAM-CNN-BiLSTM-XAI, an end-to-end deep learning method, which integrates dilated residual convolutional feature encoding, Convolutional Block Attention Module (CBAM) dual-attention block, and Bidirectional LSTM time-series modelling in order to classify five arrhythmias defined by AAMI standard. In addition, the presented deep architecture is the first to incorporate three novel elements into a single system that is clinically deployable, as per AAMI EC57 inter-patient benchmark. These include: (1) Dilated multi-scale features, (2) Channel and Temporal Dual Attention mechanism, and (3) Reject-option framework using uncertainty estimation by Monte Carlo Dropout. Trained and tested on MIT-BIH database under the AAMI DS1/DS2 patient disjoint protocol, the proposed CBAM-CNN-BiLSTM classifier exhibits a record-breaking classification performance of 99.24% with an almost negligible train/validation loss difference of 0.0007. Moreover, apart from arrhythmia classification, four cardiac conditions: Atrial Fibrillation, Ventricular Tachycardia, Myocardial Infarction, and Left Bundle Branch Block are recognized by the model's disease prediction branch using Transfer Learning techniques. The presented solution additionally includes Signal Quality Index Gate, R-peak detection via Pan-Tompkin’s algorithm and TensorFlow Lite compatibility for edge computing deployments.

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Prathibha A, Dr. Latha P H, & Sandeep Shivashettar (May-2026). CBAM-CNN-BiLSTM: A Dual-Attention Deep Learning Architecture for Explainable ECG Arrhythmia Classification and Cardiac Disease Prediction. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(5), i206-i213. https://ijnrd.org/papers/IJNRD2605819.pdf

Volume 11 Issue 5, May-2026

Pages : i206-i213

Paper Reg. ID: IJNRD_325697

Published Paper Id: IJNRD2605819

Research Area: Other area not in list

Author Type: Indian Author

Country: bengaluru, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2605819.pdf

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