Optimized time-frequency features and semi-supervised SVM to heartbeat classification

One of the most significant indicator of heart disease is arrhythmia showing heartbeat patterns. Thus, early and accurate detection of arrythmia types by categorization of heartbeats is important. In this paper, we introduce an ECG beat classifier system integrating two main parts: feature extraction and classification. For the first part, we consider the features observed in the time-frequency (t, f) plane where the ECG is projected using a variant of Stockwell transform. For the second part, the framework of semi-supervised SVM with asymmetric costs (AS3VM) has been applied for assessment of the obtained feature sets performance. Notice that four heartbeat types have been considered: normal beats (N), left and right bundle branch blocks (L and R) and premature ventricular contractions (V). The proposed method has been evaluated on PhysionNet's MIT-BIT arrythmia database. The obtained results show that the suggested approach achieves significant separability of the classes and thus, able to make prediction accuracies of 99.35% for, respectively, N, L, R and V beats.