Key result
Deep learning improves automated arrhythmia classification through end-to-end feature extraction.
Why the study?
Accurate arrhythmia diagnosis is critically important for cardiovascular disease management, necessitating a comprehensive understanding of classification techniques.
This survey provides a comprehensive overview of the transition from traditional machine learning to deep learning methodologies for automated arrhythmia classification using ECG signals.
Answers are grounded in this paper and cite their sources.
Accelerates automated ECG arrhythmia classification; leaves open computational optimization needed before clinical adoption.
This comprehensive survey presents an in-depth exploration of arrhythmia classification techniques, spanning from traditional machine learning approaches to deep learning models and hybrid methodologies. The survey covers the fundamental importance of accurate arrhythmia diagnosis, highlighting its critical role in cardiovascular disease management. Each approach's strengths, limitations, and real-world applications are meticulously discussed. The shift from feature extraction to automated feature learning facilitated by deep learning is emphasized, showcasing its transformative impact on addressing intricate challenges posed by ECG signals. The survey culminates in underlining the need for a comprehensive grasp of arrhythmia classification techniques and outlining potential future advancements. By offering a well-rounded analysis of methodologies, empirical outcomes, and existing challenges.
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Sumitha et al. (2025) conducted a review in Arrhythmia. Machine learning and deep learning techniques was evaluated. Deep learning methodologies, particularly convolutional neural networks and hybrid models, have significantly advanced automated arrhythmia classification by enabling end-to-end feature extraction.
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