ECG Arrhythmias Recognition System Based on Independent Component Analysis Feature Extraction

Abstract

Automatic recognition of cardiac arrhythmias is important for diagnosis of cardiac abnormalities. This paper presents a new approach to classification ECG signals based on feature extraction to diagnose heartbeat irregularities. We introduce the independent component analysis (ICA) feature extraction method and propose an over-complete feature extraction method combining ICA basis function's coefficients and wavelet transform coefficients. A set of relevant features are selected from the entire overcomplete features using a relevant feature selection method. The selected features are used to trained a support vector machine classifier to recognize different heartbeat arrhythmias. From computer simulations, the proposed method yields a more satisfactory classification results on the MIT-BIH arrhythmia database than the other existing methods, reaching an overall accuracy of 98.65%