A cross-correlation based approach combined with an LS-SVM classifier achieved a high classification accuracy of 95.51-96.12% for categorizing ECG beats into normal, PVC, and other beats.
Does a cross-correlation based approach with LS-SVM classifier improve ECG beat classification accuracy compared to competing algorithms?
A novel LS-SVM classifier using frequency domain cross-correlation features achieves high accuracy (95.51-96.12%) in classifying ECG beats.
The present work proposes the development of an automated medical diagnostic tool that can classify ECG beats. This is considered an important problem as accurate, timely detection of cardiac arrhythmia can help to provide proper medical attention to cure/reduce the ailment. The proposed scheme utilizes a cross-correlation based approach where the cross-spectral density information in frequency domain is used to extract suitable features. A least square support vector machine (LS-SVM) classifier is developed utilizing the features so that the ECG beats are classified into three categories: normal beats, PVC beats and other beats. This three-class classification scheme is developed utilizing a small training dataset and tested with an enormous testing dataset to show the generalization capability of the scheme. The scheme, when employed for 40 files in the MIT/BIH arrhythmia database, could produce high classification accuracy in the range 95.51-96.12% and could outperform several competing algorithms.
Dutta et al. (Wed,) conducted a other in Cardiac arrhythmia. Cross-correlation based approach with LS-SVM classifier vs. Several competing algorithms was evaluated on Classification accuracy. A cross-correlation based approach combined with an LS-SVM classifier achieved a high classification accuracy of 95.51-96.12% for categorizing ECG beats into normal, PVC, and other beats.
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