Classification of Arrhythmias Using Linear Predictive Coefficients and Probabilistic Neural Network

Abstract

Cardiac arrhythmia, which means abnormality of heart rhythm, in fact refers to disorder in electrical conduction system of the heart. The aim of this paper is to present a classifier system based on Probabilistic Neural Networks in order to detect and classify abnormal heart rates, where besides its simplicity, has high resolution capability. The proposed algorithm has three stages. At first, the electrocardiogram signals impose into preprocessing block. After preprocessing and noise elimination, the exact position of R peak is detected by multi resolution wavelet analysis. In the next step, the extracted linear predictive coefficients (LPC) of QRS complex will enter in to the classification block as an input. A Support Vector Machine classifier is developed in parallel to verify and measure the PNN classifier’s success. The experiments were conducted on the ECG data from the MIT-BIH database to classify four kinds of abnormal waveforms and normal beats such as Normal sinus rhythm, Atrial premature contraction (APC), Right bundle branch block (RBBB) and Left bundle branch block (LBBB). The results show 92.9% accuracy and 93.17% sensitivity