Classification of Arrhythmia Using Hybrid Networks
Journal of Medical Systems
Detection of abnormalities in ECG
ITIB'12 Proceedings of the Third international conference on Information Technologies in Biomedicine
Random Forest Classifier Based ECG Arrhythmia Classification
International Journal of Healthcare Information Systems and Informatics
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Automatic Detection and classification of Cardiac Arrhythmias from a limited number of ECG signals is of considerable importance in critical care or operating room patient monitoring. We propose a method to accurately classify the heartbeat of ECG signals through the Artificial Neural Networks (ANN). Feature sets are based on Heartbeat intervals, RR intervals and Spectral entropy of the ECG signal. The ability of properly trained artificial neural networks to correctly classify and recognize patterns makes them particularly suitable for use in an expert system that aids in the interpretation of ECG signals. In the present work the ECG data is taken from standard MIT-BIH Arrhythmia database. The proposed method is capable of distinguishing the normal beat and 9 different arrhythmias. The overall accuracy of classification of the proposed approach is 99.02%. The results of the analysis are found to be more accurate than the other existing methods. Detection and classification of cardiac signals is important for diagnosis of cardiac abnormalities and hence any automated processing of the ECG that assists this process would be of assistance and is the focus of this paper.