A Comprehensive Review for Predicting Arrhythmia

Arrhythmia is one of the common diseases that can cause the heart to beat irregularly, too quickly, or too slowly, and can result in death or cardiac arrest. Cardiovascular diseases (CVDs), or Heart disease, are among the most prevalent ailments globally, necessitating early detection for effective treatment and prevention of life-threatening events like cardiac arrest, heart failure, and stroke. The Electrocardiogram (ECG) is widely used to diagnose CVDs by categorizing different arrhythmia using detailed electrical signals. Recent advancements in computational methods, wearable technology, and signal processing have significantly improved arrhythmia prediction. Machine learning (ML) and Automata techniques are crucial for developing adaptive predictive models to anticipate arrhythmia early on. Research is ongoing to evaluate the accuracy of ECG arrhythmia detection using ML and data mining techniques. This study reviews current research on predicting arrhythmia early with the help of Deep Learning (DL), ML, and Artificial Intelligence (AI) methods, focusing on real-world applicability, innovation, relevance, methodology, and reproducibility.