Paper Title
Support Vector Machine Based Heartbeat Classification

In this paper, a new approach for heartbeat classification is proposed. The system uses the combination of morphological and dynamic features of ECG signal. Morphological features extracted using Wavelet transform and independent component analysis (ICA). Each heartbeat undergoes both the techniques separately. The dynamic features extracted are RR interval features. Support vector machine is used as a classifier, after concatenating the results of both the feature extraction techniques, to classify the heartbeat signals into 16 classes.Whole process is applied to both the lead signals and then the classifier results are fused to make final decision about the classification. The overall accuracy in classifying the signals from MIT-BIH arrhythmia database should be 99% in “class-oriented” evaluation and an accuracy of 86% in the “subject-oriented” evaluation.