Recognition of pathological beats in ECG signals based on Singular Value Decomposition of wavelet coefficients and support vector machine

The main goal of this work is to describe possibilities of the Singular Value Decomposition in the task of arrhythmia recognition. Many approaches try to recognize pathological beats in the time domain, our approach transforms an ECG signal from time to frequency domain, where it is reduced by Singular Value Decomposition. The new feature subspace was classified by three basic algorithms: Support Vector Machine, Linear Discriminant Analysis and Classification tree. The results were compared. Our approach increases the quality of classification and the obtained results are comparable with the results available in literature. The main aim of the proposed solution is to differentiate between physiological and pathological beats such as Premature Ventricular Contraction, Right Bundle Branch Block and Left Bundle Branch Block beats.