A comparative study on Daubechies Wavelet Transformation, Kernel PCA and PCA as feature extractors for arrhythmia detection using SVM

The electrocardiogram (ECG) plays an important role in monitoring and preventing heart attacks. In this paper, we propose and compare the use of Daubechies WT (Daubechies Wavelet Transformation), Kernel PCA (Principal Component Analysis), and PCA as feature extraction methods in improving arrhythmia signals classification. The Kernel PCA employs linear, polynomial, and Gaussian kernels. We examine Support Vector Machines (SVM) pattern classifier with various kernels including wavelet, linear, Gaussian and polynomial. The ECG signals are obtained from MIT-BIH arrhythmia database. The task is to classify or distinguish four different arrhythmias from normal ECG. The overall classification system is comprised of three components including data preprocessing, feature extraction and classification. In data preprocessing which depends on how the initial data is prepared, we reduce the baseline noise with cubic spline and cut the signal beat by beat using pivot R peak. Finally, ECG signal is classified by SVM using various kernels, our experimental results show that wavelet gives better results compared to other feature extraction methods. The accuracy of Wavelet Daubechies for feature extraction is 100% and the best kernel function for the SVM classification is Linier kernel and wavelet kernel.