Arrhythmia Detection and Classification using Morphological and Dynamic Features of ECG Signals
Arrhythmia Detection and Classification using Morphological and Dynamic Features of ECG Signals, Proc International Conf. of the IEEE Engineering in Medicine and Biology Society - EMBC, Buenos Aires, Argentina, Vol. ., pp. . - ., August, 2010.
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Computer-assisted cardiac arrhythmia detection and classification can play a significant role in the management of cardiac disorders. In this paper, we propose a new approach for arrhythmia classification based on a combination of morphological and dynamic features. Wavelet Transform (WT) and Independent Component Analysis (ICA) are applied separately to each heartbeat to extract corresponding coefficients, which are categorized as ‘morphological’ features. In addition, RR interval information is also obtained characterizing the ‘rhythm’ around the corresponding heartbeat providing ‘dynamic’ features. These two different types of features are then concatenated and Support Vector Machine (SVM) is utilized for the classification of heartbeats into 15 classes. The procedure is applied to the data from two ECG leads independently and the two results are used for the final decision, by rejecting the samples that are inconsistently classified. The proposed method was tested over the entire MIT-BIH Arrhythmias Database and it yields an overall accuracy of 99.91% on 84707 heartbeats, better than any other published results.