A Study of ECG Signal Classification using Fuzzy Logic Control

I in ECG signals, there are significant variations of waveforms in both normal and abnormal beats. In this study, we have three stages preprocessing, feature extraction (using wavelet transform) and classification (using fuzzy logic control). Signal processing techniques to detect abnormalities in ECG signals were investigated using the MIT-BIH Arrhythmia Database. The aim of developing methodology is to distinguish between normal beats and abnormal beats in an ECG signal. ECG signals were first decomposed using wavelet transform. The feature vectors were then extracted from these decomposed signals as normalized energy and entropy using wavelet analysis. To improve the classification of the feature vectors of normal and abnormal beat. The combination of wavelet decomposition and the classification using feature vectors of the beats in ECG signals separate abnormal beats from normal beats using fuzzy logic control. Evaluating the proposed algorithm, resulting in sensitivity 100 % for all except AF 90 %, specificity 100 % and total classification accuracy 97 %.