Improvement of Detection Algorithm of Extrasystoles Based on Support Vector Machine Using Multiple Features in Surface Electrocardiogram

Abstract

The electrocardiograms (ECGs) are often used as barometers of not only the state of the heart but also the state of health. However, due to their high cost and complicated measurement, they have not been used daily at home. Recently, the development of wearable devices has made it possible to easily measure ECGs, so an analysis algorithm of ECGs that can be used as a preventive medicine have been required. With regard to the automatic analysis of ECGs, while there are many studies that use two-category classification for detecting premature ventricular contraction, few studies deal with multiple classification. In this study, a method of four-category classification was proposed: normal heartbeat, premature supraventricular contraction, premature ventricular contraction, and unspecified class. In the proposed method, a model combining the support vector machine and error-correcting output cording was constructed for 13 types of features obtained from ECG signals. The result of the four-category classification shows that classification accuracy was 99.56±0.26%. The result suggests that the proposed method can be used for early detection of diseases and preventive medicine.