Early prediction of Cardiovascular Diseases using ECG signal: Review

Recent survey has pointed out that, by 2030, almost 23.6 million people will die from Cardiovascular Diseases (CVD), mainly from heart disease and stroke. These are projected to remain the single leading causes of death. One of CVD risk factors is atherosclerosis which can be predicted by myocardial ischemia detection; where this condition is caused by the lack of oxygen and nutrients to the contractile cells [3]. Ischemia changes of the ECG frequently affect the entire wave shape of ST-T complex, thus are inadequately described by isolated feature such as ST slope, ST-J amplitude and positive and negative amplitude of the T wave. In order to identify the abnormal CVDs due to the traditional risk factor such as tobacco smoking, there are several types of classifier have been used in the previous research works such as Artificial Neural Network (ANN)[21], Fuzzy Logic system[22], Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). Most of the researchers used SVM and Fuzzy Logic system in their studies [11][23].