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Electrocardiogram Dynamic Interval Feature Extraction for Heartbeat Characterization
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Author(s): Atul Kumar Verma (Dr. B. R. Ambedkar National Institute of Technology, India), Indu Saini (Dr. B. R. Ambedkar National Institute of Technology, India)and Barjinder Singh Saini (Dr. B. R. Ambedkar National Institute of Technology, India)
Copyright: 2019
Pages: 12
Source title:
Medical Data Security for Bioengineers
Source Author(s)/Editor(s): Butta Singh (Guru Nanak Dev University, India), Barjinder Singh Saini (Dr. B. R. Ambedkar National Institute of Technology, India), Dilbag Singh (Dr. B. R. Ambedkar National Institute of Technology, India)and Anukul Pandey (Dumka Engineering College, India)
DOI: 10.4018/978-1-5225-7952-6.ch012
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
In the chapter, dynamic time domain features are extracted in the proposed approach for the accurate classification of electrocardiogram (ECG) heartbeats. The dynamic time-domain information such as RR, pre-RR, post-RR, ratio of pre-post RR, and ratio of post-pre RR intervals to be extracted from the ECG beats in proposed approach for heartbeat classification. These four extracted features are combined and fed to k-nearest neighbor (k-NN) classifier with tenfold cross-validation to classify the six different heartbeats (i.e., normal [N], right bundle branch block [RBBB], left bundle branch block [LBBB], atrial premature beat [APC], paced beat [PB], and premature ventricular contraction[PVC]). The average sensitivity, specificity, positive predictivity along with overall accuracy is obtained as 99.77%, 99.97%, 99.71%, and 99.85%, respectively, for the proposed classification system. The experimental result tells that proposed classification approach has given better performance as compared with other state-of-the-art feature extraction methods for the heartbeat characterization.
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