Performance Analysis of ECG Disease Classification using SVM classifier

Biomedical signals refer to signals that are acquired from human bodies and converted into readable forms so that one canexplore them to know what is going on in human body. This paper deal with hear signal known as Electrocardiogram (ECG). Basically, ECG represents the heart's rhythm and electrical activity. One kind of disorder presented in ECG is known as Arrhythmia. The dataset for this work has been obtained from MIT-BIH Arrhythmia Database (Massachusetts Institute of Technology and Beth Israel Hospital). The dataset used in this paper has been retrieved from PhysioNet. In this study Support Vector Machine (SVM) classifier has been utilized in order to achieve the highest accuracy. Finally, the results are compared to existing methods in which the data of six h u m a n subjects was included.