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Effective and efficient detection of premature ventricular contractions based on variation of principal directions

Zarei, Roozbeh, He, Jing, Huang, Guangyan and Zhang, Yanchun 2016, Effective and efficient detection of premature ventricular contractions based on variation of principal directions, Digital signal processing: a review journal, vol. 50, pp. 93-102, doi: 10.1016/j.dsp.2015.12.002.

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Title Effective and efficient detection of premature ventricular contractions based on variation of principal directions
Author(s) Zarei, Roozbeh
He, Jing
Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
Zhang, Yanchun
Journal name Digital signal processing: a review journal
Volume number 50
Start page 93
End page 102
Total pages 10
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-03
ISSN 1051-2004
Summary Classification of electrocardiogram (ECG) data stream is essential to diagnosis of critical heart conditions. It is vital to accurately detect abnormality in the ECG in order to prevent possible beginning of life-threatening cardiac symptoms. In this paper, we focus on identifying premature ventricular contraction (PVC) which is one of the most common heart rhythm abnormalities. We use "Replacing" strategy to check the effects of each individual heartbeat on the variation of principal directions. Based on this idea, an online PVC detection method is proposed to classify the new arriving PVC beats in the real-time and online manner. The proposed approach is tested on the MIT-BIH arrhythmia database (MIT-BIH-AR). The PVC detection accuracy was 98.77%, with the sensitivity and positive predictivity of 96.12% and 86.48%, respectively. These results are an improvement on previous reported results for PVC detection. In addition, our proposed method is effective in terms of computation time. The average execution time of our proposed method was 3.83 s for a 30 min ECG recording. It shows the capability of the classifier to detect abnormal PVCs in online manner.
Language eng
DOI 10.1016/j.dsp.2015.12.002
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083553

Document type: Journal Article
Collection: School of Information Technology
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