A joint CNN-Bi-LSTM-transformer architecture achieved an accuracy of 91.24% and a macro-F1 score of 83.01% for multi-label classification of 27 arrhythmia classes from 12-lead ECGs.
Does a joint CNN-Bi-LSTM-transformer architecture with SHAP explanations improve multi-label arrhythmia detection accuracy in 12-lead ECG recordings?
A novel deep learning architecture combining CNN, Bi-LSTM, and transformer layers with SHAP explanations provides highly accurate, computationally efficient, and clinically interpretable multi-label arrhythmia detection from 12-lead ECGs.
Interpretable, automated Artificial Intelligence (AI) solutions are essential for accurate 12-lead electrocardiogram (ECG) arrhythmia classification because they remove the time-consuming and inconsistent aspects of manual interpretation. Current models are limited in complexity, data variety, and validation. This paper proposes a novel Deep Learning (DL) architecture that combines Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTMs), and transformer layers to jointly extract morphological, temporal, and spatial patterns from ECG signals. The model was trained and evaluated on the PhysioNet/Computing in Cardiology Challenge 2020 dataset, comprising more than 43, 000 multi-label ECG recordings across 27 arrhythmia classes. It achieved an accuracy of \: 91. 24\%, a macro-F1 score of \: 83. 01\%, and an Area Under the ROC Curve (AUC) exceeding \: 0. 98 for life-threatening arrhythmias such as Ventricular Premature Beats (VPB) and Atrial Fibrillation (AF). To ensure clinical transparency, the model integrates SHAP (SHAPley Additive exPlanations), enabling case-by-case interpretability by attributing predictions to physiologically relevant waveform segments and ECG leads. This approach aligns with cardiologists’ diagnostic reasoning and supports real-world decision-making. Additionally, the model is computationally efficient, with a footprint of \: <2\: MB and inference latency of \: <10\: ms, enabling deployment in telemedicine, wearable monitoring systems, and critical care settings. The proposed framework achieves high diagnostic performance, robustness to class imbalance, and human-level interpretability simultaneously, providing a reliable, scalable solution for automated ECG analysis. These findings advance the application of explainable DL algorithms in cardiovascular diagnostics.
Al-Bairmani et al. (Thu,) conducted a other in Cardiac arrhythmias (n=43,101). Joint CNN-Bi-LSTM-transformer architecture was evaluated on Arrhythmia classification accuracy. A joint CNN-Bi-LSTM-transformer architecture achieved an accuracy of 91.24% and a macro-F1 score of 83.01% for multi-label classification of 27 arrhythmia classes from 12-lead ECGs.