ECG signal classification based on SVM

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Date
2016
ORCID
Advisor
Referee
Mark
Journal Title
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Volume Title
Publisher
Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract
Cardiovascular diseases nowadays represent the most common cause of death in Western countries. Long-term ECG recording is modern method, because it allows to detect sporadically occurring pathology. We designed an automatic classifier to detect five pathologies (AAMI standard) by SVM method. The classifier was tested on the entire MIT-BIH Arrhythmia Database with an accuracy of 99.17 %. We also compared the quality of parameters entering the classifier.
Description
Citation
Proceedings of the 22nd Conference STUDENT EEICT 2016. s. 365-369. ISBN 978-80-214-5350-0
http://www.feec.vutbr.cz/EEICT/
Document type
Peer-reviewed
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en
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Defence
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© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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