Revolutionizing Cardiac Care: A Comprehensive Review of ECG-Based Arrhythmia Prediction Techniques

Cardiac arrhythmia has now become a common condition that affects millions of people worldwide. Arrhythmia can be detected and anticipated early, allowing for more effective treatment and better comprehension of the results. The various machine learning–based methods for detecting various cardiac arrhythmias are reviewed in this investigation. An enormous assortment of electrocardiogram (ECG) information from people with different arrhythmias was utilized to prepare the models broke down in this review. The studies employ various machine learning and deep learning methods to accurately diagnose arrhythmia and comprehend intricate patterns in electrocardiogram (ECG) data. In addition, we investigate how various electrocardiogram (ECG) signal qualities affect the performance of various models. This research takes the employed studies into consideration and compare their results.