A precise ambulatory ECG arrhythmia intelligent analysis algorithm based on support vector machine classifiers

As we know, Electrocardiogram (ECG) supervising is the most efficient and effective way of preventing Cardiovascular diseases. ECG arrhythmia intelligent analysis system will not only save time but also provide accurate diagnosis for physicians. Recently, we have developed ambulatory ECG (AECG) arrhythmia intelligent analysis software (AIAS) by Visual Basic 6.0 with the total accuracy 95.9%. The purpose of this paper is to develop an algorithm for recognizing and classifying normal beat, ventricular premature beat (VPB) and atrial premature beat (APB) for purpose of optimize our AIAS. In order to do so, we use the ECG data from MIT-BIH arrhythmia database and clinical data collected in the hospital. The method of continuous wavelet transformation has been used for feature extraction. By means of feature extraction, we get feature vectors for SVM to analyze. Finally, we make great progress in the accuracy and promote the accuracy from 95.9% to 99% with SVM classifiers and wavelet transformation.