ECG beat classification method for ECG printout with Principle Components Analysis and Support Vector Machines

ECG is an electric signal which is generated from human heart. It is used for investigate some of abnormal heart function. For this paper the shape of ECG is used to classify ECG beat in four types such as normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R) and ventricular premature beat (V). To extract the shape of ECG, the discrete wavelet transform with level 3 of Daubechies 1 is used after digital filter was applied to remove noise from ECG signal. After that PCA(Principle Components Analysis) and SVM (Support Vector Machines) are adapted to create model of classifier for using with paper based ECG printout. The ECG image from ECG printout is processed by some image processing techniques such as red grid removing, noise rejection, image thinning and time-series ECG extraction to obtain the time-series ECG signal before classification. Based on MIT-BIH arrhythmia database which using for SVM training and evaluation, the performance of this classifier is 99.6367% with LIBSVM.