Key result
CardioMultiNet achieves ~98% accuracy in diagnosing cardiovascular diseases from multi-lead ECGs.
Why the study?
Traditional deep learning models for automated ECG diagnosis isolate local morphology, dynamic behaviour, and inter-lead contextual dependencies, creating a trade-off between performance, interpretability, and computational efficiency.
Does the CardioMultiNet hybrid CNN-Bi-LSTM-Transformer architecture improve diagnostic classification accuracy and interpretability for cardiovascular disease using multi-lead ECG datasets?
Does the CardioMultiNet hybrid CNN-Bi-LSTM-Transformer architecture improve diagnostic classification accuracy and interpretability for cardiovascular disease using multi-lead ECG datasets?
The CardioMultiNet hybrid CNN-Bi-LSTM-Transformer model achieves 98.2% accuracy and 0.992 AUC across multiple public ECG datasets while incorporating uncertainty estimation and explainability for automated cardiovascular disease diagnosis.
Since cardiovascular diseases (CVDs) are the leading cause of death in the world, it is high time to independently produce an automated diagnostic system of ECG signals that determines heart disease. Traditional deep learning models, such as CNNs, LSTMs, and other Transformer-based models, isolate local morphology (through convolution), dynamic behaviour (through recurrent units), and inter-lead contextual dependencies, creating a trade-off between performance, interpretability, and computational efficiency. This work introduces CardioDeepAI, a complete multi-lead ECG analysis framework comprising CardioMultiNet, a CNN-LM-Transformer hybrid architecture tailored to this task. The model uses convolutional layers to extract morphological features, stacked Bi-LSTM modules to model temporal dependencies, and Transformer-based attention mechanisms to capture global context and lead correlations. We also introduce a robust uncertainty-aware mechanism and explainability methods such as Grad-CAM, SHAP, and attention visualisation to improve robustness and provide insightful visualisations. On the PTB-XL, MIT-BIH, and CPSC datasets, our model achieves an accuracy of 98.2%, an F1-score of 98.1%, an AUC of 0.992, and a Matthews correlation coefficient (MCC) of 0.963, providing a strong basis for real-time clinical access and wearable health applications.
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Mounika et al. (2026) studied Cardiovascular disease (Normal, Arrhythmia, Myocardial Infarction). CardioMultiNet (CNN-Bi-LSTM-Transformer hybrid deep learning model within the CardioDeepAI framework) vs. Traditional machine learning and standalone deep learning baseline models (CNN, LSTM, Transformer) was evaluated on Diagnostic classification accuracy for cardiovascular diseases across PTB-XL, MIT-BIH, and CPSC datasets (Accuracy 98.2%, F1-score 98.1%, AUC 0.992, MCC 0.963). CardioMultiNet achieved 98.2% accuracy, 98.1% F1-score, and 0.992 AUC in diagnosing cardiovascular diseases from multi-lead ECGs across the PTB-XL, MIT-BIH, and CPSC datasets.
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