Diagnostic of ECG Arrhythmia using Wavelet Analysis and K-Nearest Neighbor Algorithm

Abstract

The automatic electrocardiogram (ECG) beat classification is a very useful tool to timely diagnosis of dangerous heart conditions. In this paper, we have implemented an automatic ECG heartbeats classifier based on the K Nearest Neighbor algorithm (KNN). The segmentation of ECG signals has been performed by Discrete Wavelet Transform (DWT). The considered categories of beats are: Normal (N), Premature Ventricular Contraction (PVC), Atrial Premature Contraction (APC), Right Bundle Branch Block (RBBB) and Left Bundle Branch Block (LBBB). The validation of the presented KNN based classifier has been achieved using ECG data from MIT-BIH arrhythmia database. We have obtained the good classification performances, in terms of the calculated values of the specificity and the sensitivity of the classifier for several pathological heartbeats and the global classification rate, which is equal to 98,71%.