Arrhythmia Modern Classification Techniques: A Review

Mohamed Saber^1*, Mostafa Abotaleb²

¹Electronics and Communications Engineering Dep., Faculty of Engineering, Delta University for

Science and Technology, Gamasa City, Mansoura, Egypt

²Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia

Emails: Mohamed.saber@deltauniv.edu.eg ; abotalebmostafa@bk.ru 

Abstract

Artificial intelligence methods are utilized in biological signal processing to locate and extract interesting data. The examination of ECG signal characteristics is crucial for the diagnosis of cardiac disease. This heart condition, known as arrhythmia, is quite prevalent. To put it simply, an irregular heartbeat is known as cardiac arrhythmia. It manifests itself when the heart beats abnormally (too slowly, too quickly, or erratically) for no apparent reason. Specifically, the ECG features of the PR, QRS, T, PQ, QT, RR, and cardiac frequency and rhythm are analyzed to diagnose cardiac arrhythmias. The performance of several arrhythmia classification and detection models is analyzed in this work through extensive simulations, emphasizing the most recent developments in this field. Ultimately, the research provides new perspectives on arrhythmia classification methods to address the shortcomings of the current approaches.

Keywords: ECG ; Arrhythmia; classification; Neural Network; Optimization