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An automatic R and T peak detection method based on the combination of hierarchical clustering and discrete wavelet transform

An automatic R and T peak detection method based on the combination of hierarchical clustering and discrete wavelet transform
An automatic R and T peak detection method based on the combination of hierarchical clustering and discrete wavelet transform
The detection and delineation of QRS-complexes and T-waves in electrocardiogram (ECG) is an important task because these features are associated with the cardiac abnormalities including ventricular arrhythmias that may lead to sudden cardiac death. In this paper, we propose a novel method for the R-peak and the T-peak detection using hierarchical clustering and discrete wavelet transform (DWT) from the ECG signal. In the first step, a template of the single ECG beat is identified. Secondly, all R-peaks are detected by using hierarchical clustering. Then, each corresponding T-wave boundary is delineated based on the template morphology. Finally, the determination of T wave peaks is achieved based on the modulus-maxima analysis (MMA) of the DWT coefficients. We evaluated the algorithm by using all records from the MIT-BIH arrhythmia database and QT database. The R-peak detector achieved a sensitivity of 99.89%, a positive predictivity of 99.97% and 99.83% accuracy over the validation MIT-BIH database. In addition, it shows a sensitivity of 100%, a positive predictivity of 99.83% in manually annotated QT database. It also shows 99.92% sensitivity and 99.96% positive predictivity over the automatic annotated QT database. In terms of the T-peak detection, our algorithm is verified with 99.91% sensitivity and 99.38% positive predictivity in manually annotated QT database.
2168-2194
Chen, Hanjie
7a5b6697-7e34-4787-9254-7995f013e94a
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Chen, Hanjie
7a5b6697-7e34-4787-9254-7995f013e94a
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Chen, Hanjie and Maharatna, Koushik (2020) An automatic R and T peak detection method based on the combination of hierarchical clustering and discrete wavelet transform. IEEE Journal of Biomedical and Health Informatics. (doi:10.1109/JBHI.2020.2973982).

Record type: Article

Abstract

The detection and delineation of QRS-complexes and T-waves in electrocardiogram (ECG) is an important task because these features are associated with the cardiac abnormalities including ventricular arrhythmias that may lead to sudden cardiac death. In this paper, we propose a novel method for the R-peak and the T-peak detection using hierarchical clustering and discrete wavelet transform (DWT) from the ECG signal. In the first step, a template of the single ECG beat is identified. Secondly, all R-peaks are detected by using hierarchical clustering. Then, each corresponding T-wave boundary is delineated based on the template morphology. Finally, the determination of T wave peaks is achieved based on the modulus-maxima analysis (MMA) of the DWT coefficients. We evaluated the algorithm by using all records from the MIT-BIH arrhythmia database and QT database. The R-peak detector achieved a sensitivity of 99.89%, a positive predictivity of 99.97% and 99.83% accuracy over the validation MIT-BIH database. In addition, it shows a sensitivity of 100%, a positive predictivity of 99.83% in manually annotated QT database. It also shows 99.92% sensitivity and 99.96% positive predictivity over the automatic annotated QT database. In terms of the T-peak detection, our algorithm is verified with 99.91% sensitivity and 99.38% positive predictivity in manually annotated QT database.

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08999502 - Accepted Manuscript
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Accepted/In Press date: 10 February 2020
e-pub ahead of print date: 14 February 2020

Identifiers

Local EPrints ID: 438399
URI: http://eprints.soton.ac.uk/id/eprint/438399
ISSN: 2168-2194
PURE UUID: 0bbdda6c-0800-4660-8e8c-076106caf271
ORCID for Hanjie Chen: ORCID iD orcid.org/0000-0001-8024-8804

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Date deposited: 09 Mar 2020 17:31
Last modified: 07 Oct 2020 02:17

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Contributors

Author: Hanjie Chen ORCID iD
Author: Koushik Maharatna

University divisions

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