Publication detail

ECG signal classification based on SVM

SMÍŠEK, R. KOLÁŘOVÁ, J.

Original Title

ECG signal classification based on SVM

English Title

ECG signal classification based on SVM

Type

conference paper

Language

en

Original 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.

English 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.

Keywords

ECG classification, support vector machines, SVM, MIT-BIH database

Released

28.04.2016

Publisher

Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií

Location

Brno

ISBN

978-80-214-5350-0

Book

Proceedings of the 22st Conference STUDENT EEICT 2016

Edition number

první

Pages from

365

Pages to

369

Pages count

5

URL

BibTex


@inproceedings{BUT124701,
  author="Radovan {Smíšek} and Jana {Kolářová}",
  title="ECG signal classification based on SVM",
  annote="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.",
  address="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  booktitle="Proceedings of the 22st Conference STUDENT EEICT 2016",
  chapter="124701",
  howpublished="online",
  institution="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  year="2016",
  month="april",
  pages="365--369",
  publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
  type="conference paper"
}