Machine Learning and Deep Learning Approaches for Automated ECG Based Cardiac Arrhythmia Diagnosis: A Review

Automated arrhythmia detection is critical for the timely management of cardiovascular disorders. Recent advancements in machine learning (ML) and deep learning (DL) have significantly enhanced the diagnostic capabilities of electrocardiogram (ECG) analysis. This paper reviews current state-of-the-art methodologies, contrasting classical ML algorithms—which rely on handcrafted features such as DWT and HRV—with modern DL architectures that automatically extract hierarchical representations from raw signals. We provide a comparative analysis of these approaches, evaluating their diagnostic performance, computational requirements, and reliance on large-scale annotated datasets. Finally, this study addresses existing challenges and outlines future research directions toward developing more interpretable, robust, and clinically applicable arrhythmia monitoring systems.

کلیدواژه ها:

Electrocardiogram (ECG) ، arrhythmia ، Machine learning ، Deep learning ، Convolutional neural networks (CNN)