Abstract

Background: Arrhythmia-induced cardiomyopathy (AIC) is a variation of cardiovascular disease with significant global morbidity and mortality. Arrhythmia is important to detect in time to allow for suitable intervention and clinical management, and this can be accomplished using electrocardiogram (ECG) signals. However, existing approaches face critical challenges to be solved, including (i) similar arrhythmias being hard to distinguish, (ii) misdiagnosis which can lead to life-threatening conditions, and (iii) single-lead ECG possibly missing subtle differences between arrhythmias. Methods: The study proposes a lightweight and efficient deep learning framework using selected ECG leads extracted from the PhysioNet large-scale 12-lead ECG arrhythmia database. The signals undergo preprocessing involving baseline wander removal and noise reduction. A Saliency-Guided Depthwise-Separable Dilated Network (SG-DSN) is introduced for feature extraction and classification. The model employs multi-scale dilated convolutions with saliency attention to capture discriminative ECG features, followed by softmax classification. The system not only classifies arrhythmia types but also flags potential AIC risk cases for early clinical intervention. Results: Experimental evaluation demonstrates improved arrhythmia classification performance, reduced computational complexity, and enhanced suitability for real-time clinical applications. Conclusion: Thus, the proposed framework is an efficient, scalable, and accurate solution for early detection and risk alerting of AIC.

IPC Classification

Keywords