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An automatic R-peak detection method based on hierarchical clustering

An automatic R-peak detection method based on hierarchical clustering
An automatic R-peak detection method based on hierarchical clustering
The detection of R peaks in electrocardiogram (ECG) is an important task because R peaks can be used to identify the heart rate in order to detect different types of cardiac abnormalities including arrhythmias. This paper proposes a novel R peak detection algorithm from ECG based on a machine learning algorithm named hierarchical clustering. We evaluate the algorithm by using the 48 half-hour ECG records of MIT-BIT arrhythmias database and compare with different techniques. Our R peak detector achieves average detection accuracy of 99.83%, a sensitivity of 99.89% and a positive predictive value of 99.94% over the validation database and the results also show the proposed algorithm significantly reduces the false detection of the R-peaks.
IEEE
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 (2019) An automatic R-peak detection method based on hierarchical clustering. In 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE. 4 pp . (doi:10.1109/BIOCAS.2019.8919208).

Record type: Conference or Workshop Item (Paper)

Abstract

The detection of R peaks in electrocardiogram (ECG) is an important task because R peaks can be used to identify the heart rate in order to detect different types of cardiac abnormalities including arrhythmias. This paper proposes a novel R peak detection algorithm from ECG based on a machine learning algorithm named hierarchical clustering. We evaluate the algorithm by using the 48 half-hour ECG records of MIT-BIT arrhythmias database and compare with different techniques. Our R peak detector achieves average detection accuracy of 99.83%, a sensitivity of 99.89% and a positive predictive value of 99.94% over the validation database and the results also show the proposed algorithm significantly reduces the false detection of the R-peaks.

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More information

Published date: 5 December 2019
Venue - Dates: IEEE Biomedical Circuits and Systems Conference (BioCAS), 2019-10-17 - 2019-10-19

Identifiers

Local EPrints ID: 436527
URI: http://eprints.soton.ac.uk/id/eprint/436527
PURE UUID: efb8c3c1-6e38-47f9-b77a-afb2bc2a8f6b
ORCID for Hanjie Chen: ORCID iD orcid.org/0000-0001-8024-8804

Catalogue record

Date deposited: 12 Dec 2019 17:30
Last modified: 01 Feb 2020 01:41

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