Deciphering ECG Signals

Abstract

One of the leading causes of death worldwide is heart disease. Therefore, in order to slow the rising death rate, early diagnosis of heart disorders is essential. The electrocardiogram (ECG) is a commonly used diagnostic tool for a variety of cardiac conditions, including irregular heartbeats (arrhythmias). It is quite challenging to identify the abnormal ECG signals' properties, nevertheless, due to their non-linearity and complexity. Furthermore, manually verifying these ECG signals could take a lot of time. To get over these restrictions, researchers have developed a quick and precise classifier that performs better than other well-known classifiers at simulating a cardiologist's diagnosis in order to distinguish between normal and pathological ECG signals from a single lead ECG signal. Analyzing and processing ECG data is a crucial step in the diagnosis of cardiovascular diseases. The main goal of this chapter is to understand the classification of healthy and sick people through popular machine learning-based methods.