Abstract

The main objective of this activity is to identify patients who have different cardiac vascular arrhythmias and test them to diagnose abnormalities. A difficult challenge is the high-accuracy classification of ECG arrhythmia. Healthcare applications where early detection of irregularities on ECG might be important in patient monitoring have lately made Deep Learning (DL) a focus of research. Pre-processing the ECG signal, extracting the characteristics, and then optimizing the features and categorization of the arrhythmia are all necessary for its detection. The topics of ECG signal preprocessing, feature extraction, optimization, and classification are normally covered in deep learning models. To classify the ECG signal, this work gives an in-depth overview analysis of the most recent DL techniques. In the meanwhile, the benefits and drawbacks of various techniques in various applications are compiled to serve as a guide and a point of reference for future study.