Early Prediction of Cardiovascular Diseases Using ECG signal: Review Nurul Hikmah Kamaruddin #1 , M.Murugappan #2 , Mohammad Iqbal Omar #3 # School of Mechatronic Engineering, University Malaysia Perlis, Kampus Pauh, 02600 Ulu Pauh, Perlis, MALAYSIA. 1 nurulhikmah88@gmail.com, 2 murugappan@unimap.edu.my, 3 iqbalomar@unimap.edu.my Abstract- 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]. Keywords- Cardiovascular Disease (CVD); Myocardial Ischemia; Electrocardiogram (ECG;, Discrete Wavelet Transform (DWT); Support Vector Machine (SVM) I. INTRODUCTION 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). CVD is the heart abnormalities which mean malfunctioning and dysfunction of heart in which it eventually leads to less oxygen supply to all vital parts of the body. It affects oxygen transportation to brain, lungs, internal and heart itself and supply of less oxygen leads to cause more problems in the body [2]. The natural functioning of the heart and its generation of electrical impulses are affected by the risk factor such as smoking will it leads to induce several CVD diseases and it increases the dizziness with more palpitation. Finally abnormal blood flows with a chance of heart attack are the more severe conditions. According to the recent survey, 17.1 million people were died from CVDs in 2004, representing 29 % of all global deaths. Of these deaths, an estimation of 7.2 million was due to coronary heart diseases and 5.7 million were due to stroke [2]. Low- and middle-income countries are disproportionally affected, 82% of CVD deaths take place in low- and middle-income countries and occur almost equally in men and women. 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. The largest percentage of death due to CVDs will occur in the Eastern Mediterranean Region [2].Currently, the best practice for reducing human mortality rates caused by complex diseases is to detect their symptoms at early stages. Through the early recognition of symptoms one can get the most effective clinical treatment for the best outcome [4]. The medical treatment has been supported by computerized processes. Signals recorded from the human body provided valuable information about the activities of its organ. Their characteristic shape and spectral property can be correlated with a normal or pathological function [5]. Most of the previous research focuses on ECG signal processing in order to predict the cardiovascular disease in early stage. In this paper, we present a review of the recent advancement in prediction of cardiovascular disease research using ECG signal analysis; in specific to the database used and the designation of data acquisition, features extraction and classification methodologies. The goal of this review is to access and improve the efficiency of the prediction system for the early stage of cardiovascular disease with the knowledge of the current advancement in the technology. II. CARDIOVASCULAR DISEASE RISK FACTORS There are several risk factors that associated to Cardiovascular Diseases, such as smoking, high levels of cholesterol and triglycerides in the blood, high blood pressure, lack of exercise, obesity, diabetes [1]. Smoking accelerates the process of atherosclerosis, or the build-up of fatty deposits and cholesterol in the arteries. Each time a person smokes a cigarette, the blood vessels become sticky from the chemicals in the tobacco smoke and this leads to fat collecting and sticking to the artery walls. Most of the other cardiovascular diseases and coronary 978-1-4673-5160-7/12/$26.00 ©2012 IEEE S2-1 2012 IEEE Student Conference on Research and Development 48