ECG analysis based on PCA and Support Vector Machines

Cardiovascular diseases is one of the main courses of death around the world. Electrocardiogram (ECG) supervising is the most important and efficient way of preventing heart attacks. Machine monitoring and analysis of ECG is becoming a major topic of the modern medical research. In this paper, we propose a system to detect cardiac arrhythmia using the ECG data form MIT-BIH database as a reference. The purpose of this paper is to develop an algorithm for recognizing and classifying normal beat, left bundle branch block beat, right bundle branch block beat and premature ventricular contraction (PVC). In order to do so, we extract more than 6000 signals from the original database, each signal representing a single and complete heart beat. We extract the principal characteristics of the signal by means of the principal component analysis (PCA) technique. Support vector machine (SVM) has a major predominance over other classification methods in complicated problems. SVM method is applied to classify the ECG data into the 4 categories of heart diseases. Base on this idea, we achieved better results in comparison with other pattern classification method from our computer simulations