Arrhythmia Detection on ECG Signals by Using Empirical Mode Decomposition

Abstract

One of the main causes of sudden deaths is heart disease. Early detection and treatment of cardiac arrhythmias prevent the problem from reaching sudden deaths. The purpose of this study is to develop an arrhythmia detection algorithm based on Empirical Mode Decomposition (EMD). This algorithm consists of four steps: Preprocessing, Empirical Mode Decomposition, feature extraction and classification. Six arrhythmia types were used for differentiate normal and arrhythmic signals obtained from the MIT-BIH Arrhythmia database. These are normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), paced beat and atrial premature beats (APB). Three different classifiers were used to classify ECG signals. The method achieves better result with accuracy of 87% using linear discriminant analysis (LDA) classifier for detection of normal and arrhythmic signals.