Study and analysis on detection, classification, and prediction of cardiac arrhythmia using soft computing tool

Abstract

Cardiac Arrhythmia (CA) is a disorder of heartbeat or rhythm, that happens when the electrical signals that synchronize the heartbeats do not function properly. The Electro Cardio Gram (ECG) is the electrical realization of the expanding and contracting action of the heart and can be registered easily with the electrodes placed near the chest. Hence, due to the complexity of analyzing the huge number of signals in ECG records, it has become one of the major challenges to cardiologists to make early and accurate diagnoses and prognoses. Therefore, there is an essential need for accurate automatic arrhythmia classification. According to the records of the World Health Organization (WHO), 4.5 million CA patients are reporting alone in the United States. Therefore, it is stated as one of the most common reasons for death worldwide and it is very essential to the early diagnosis and prevention of CA. Hence, this research article mainly focuses to analyse the various methods used for the classification, early diagnosis, and prevention of CA. This research presents the overview of a few research articles suggesting different methods based on various fields like IOT, Machine Learning (ML) approaches, Deep Learning (DL) approaches, and so on for the automatic detection of Cardiac Arrhythmia. The literature work mainly focuses on various early Detection, prediction, and classification techniques for CA. The research gaps were also analyzed from these papers and elaborated for further research work which can be helpful for society.