Early predictionof cardiovascular diseases using ECG signal: A review

Abstract

Electrocardiography is considered a representative signal of cardiac physiology. ECG signal analysis can provide lots of information about heart condition whether it is normal and abnormal. Cardiovascular Disease (CVD) is one of the major leading causes of mortality in the worldwide including Malaysia. The main cardiovascular diseases are heart attack, angina, stroke and peripheral vascular disease (PVD). There are many risk factors that can be major reason for the cause of heart/cardiovascular diseases and also premature death. Recent survey has pointed out that, by 2030, almost 23.6 million people will die from CVDs, mainly from heart disease and stroke. These are projected to remain the single leading causes of death.Most of the other cardiovascular diseases and coronary heart diseases are caused by the progression of atherosclerosis. One of the progressions of atherosclerosis is myocardial ischemia; where this condition is caused by the lack of oxygen and nutrients to the contractile cells [3]. Usually, ischemia is expressed in the ECG signal as ST segment deviations and/or T wave changes [15].These ST segment morphology compatible with ischemia (ischemic changes) usually obtained by recording the ECG signal over long period of time. 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 can be 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 have used SVM and Fuzzy Logic system for CVDs classification using ECG signals [3] [10] [11][23].