Advancements In Arrhythmia Classification: A Comprehensive Survey Of Machine Learning Techniques

Abstract

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.