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Title: | Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification |
Authors: | DATTA, Saibal |
Keywords: | Publications Electrocardiogram (ECG) Beat classification Cross-correlation Cross-spectral density Support vector machine Saibal Datta Electrical engineering |
Issue Date: | 12-Apr-2010 |
Publisher: | Elsevier |
Abstract: | 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,whenemployed 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. |
URI: | http://hdl.handle.net/123456789/1183 |
ISSN: | 1161–1169 |
Appears in Collections: | Electrical Engineering (Publications) |
Files in This Item:
File | Description | Size | Format | |
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IJ2_Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification.pdf | 843.08 kB | Adobe PDF | View/Open |
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