Electrocardiogram signals classification using discrete wavelet transform and support vector machine classifier
Youssef Toulni, Benayad Nsiri, Belhoussine Drissi Taoufiq
Abstract
The electrocardiography allowed us to make a diagnosis of several cardiovascular diseases by representing the electrical activity of the heart over time; this representation is called the electrocardiogram (ECG) signal. In this study we have proposed a model based on the processing of the ECG signal by the wavelet decomposition using discrete wavelet transform (DWT). This decomposition firstly makes it possible to denoise the signal then to extract the statistical features from the approximation coefficients of the denoised signal and finally to classify the data obtained in a support vector machine (SVM) classifier with cross validation for more credibility. After having tested this model with different mother wavelets at different scales, the accuracies at the fourth scale are high and the best accuracy obtained is 87.50%.