In the process of ventricular premature beat (PVC) and normal sinus rhythm (NSR) identification base on electrocardiogram (ECG), there exists problems like negative effect from ECG rhythm and low recognition rate. This paper proposes the electrocardiogram PVC classification algorithm based on support vector machine (SVM) and wavelet algorithm. The algorithm uses the wavelet transform to analyze ECG beating model, which is not influenced by the change of ECG waveform. The two feature sets respectively compose of statistical parameters of the wavelet coefficients and the selected wavelet coefficients. PVC and NSR are analyzed by using SVM. The experimental results show that this method improves the recognition rate of ECG.
