@article {104719,
	title = {Enhanced ECG arrhythmia detection with deep learning and multi-head attention mechanism},
	journal = {Computers and Electrical Engineering},
	volume = {131},
	year = {2026},
	abstract = {
	Detecting arrhythmias via electrocardiograms (ECGs) is vital for healthcare. While deep learning has advanced classification, capturing critical patterns in complex data remains challenging. We propose Res_Bi-LSTM_MHA, a novel model integrating a multi-head self-attention (MHA) mechanism to selectively focus on relevant signal segments. This enhances the capture of subtle features often missed by conventional methods. By combining Residual Networks (ResNet) for robust feature extraction with Bidirectional Long Short-Term Memory (Bi-LSTM) for temporal dependencies, our approach significantly improves accuracy. We evaluated the model at subject and record levels using the China Physiological Signal Challenge (CPSC 2018), St. Petersburg Institute of Cardiological Technics (INCART), and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) databases. The model achieved an F1 score of 98.01\% and 99.42\% accuracy on the MIT-BIH dataset. Our results demonstrate that effectively utilizing attention mechanisms offers a substantial improvement in arrhythmia classification.

},
	url = {https://doi.org/10.1016/j.compeleceng.2026.110957},
	author = {Kerdoudi, Saoueb and Larbi GUEZOULI and Dilekh, Tahar}
}
