Key result
The proposed CNN-GNN-BiLSTM framework achieved 96.0% overall accuracy and up to 99.89% accuracy on the MIT-BIH dataset for automated ECG arrhythmia classification.
Why the study?
Early and accurate detection of cardiac arrhythmias is crucial for preventing severe cardiovascular events.
Does a CNN-GNN-BiLSTM integrated framework improve ECG arrhythmia detection accuracy compared to conventional deep learning approaches in benchmark ECG datasets?
Does a CNN-GNN-BiLSTM integrated framework improve ECG arrhythmia detection accuracy compared to conventional deep learning approaches in benchmark ECG datasets?
A novel CNN-GNN-BiLSTM deep learning framework achieves high accuracy for automated ECG arrhythmia detection, offering a scalable solution for AI-driven cardiac monitoring.
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AI models may enhance ECG arrhythmia detection; leaves open prospective clinical validation before practice change.
Mahajan et al. (2025) studied Cardiac arrhythmias. CNN-GNN-BiLSTM integrated framework vs. Conventional deep learning approaches was evaluated on Overall accuracy. The proposed CNN-GNN-BiLSTM framework achieved 96.0% overall accuracy and up to 99.89% accuracy on the MIT-BIH dataset for automated ECG arrhythmia classification.
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