Machine learning based cardiac arrhythmia diagnosis using statistical and dynamic features of ecg and ppg signal

Abstract

The heart is a spectacular and muscular organ which pumps oxygen and nutritious blood through blood vessels to the body tissues to save our life. Heart disease is one of the significant causes of death around the world. Heart disease is a type of disorder that affects the functions of heart. It occurs because of high workload, mental stress, smoking, physical inactivity, and other problems. The heart diseases are Cardiac arrhythmia, Heart valve disease, congenital heart disease, Myocardial Infarction, and Congestive heart failure. Among them, Cardiac Arrhythmia is the leading cause of mortality, in which the heart beats too slowly or quickly. The fluctuation in a heartbeat leads to cardiac arrest, which results in sudden death. The development of the Cardiac Arrhythmia Diagnosis System is a demanding research area for the early detection of arrhythmia disease. newlineThe major objective of this work is to classify the arrhythmia disease with Electrocardiogram (ECG), and Photoplethysmography (PPG) signals using a machine learning approach. The objectives of the research work are newline