A Recognition of Ecg Arrhythmias Using Artificial Neural Networks

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dc.contributor.authorÖzbay, Yüksel
dc.contributor.authorKarlık, Bekir
dc.date.accessioned2020-03-26T16:36:57Z
dc.date.available2020-03-26T16:36:57Z
dc.date.issued2001
dc.departmentSelçuk Üniversitesien_US
dc.description23rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society -- OCT 25-28, 2001 -- ISTANBUL, TURKEYen_US
dc.description.abstractIn this study, Artificial Neural Networks (ANN) has been used to classify the ECG arrhythmias. Types of arrhythmias chosen from MIT-BIH ECG database to train ANN include normal sinus rhythm, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced beat, right bundle branch block, left bundle branch block, atrial fibrillation, and atrial flutter have been as. The different structures of ANN have been trained by arrhythmia separately and also by mixing these 10 different arrhythmias. The most appropriate ANN structure is used for each class to test patients' records. The ECG records of 17 patients whose average age is 38.6 were made in the Cardiology Department, Faculty of Medicine at Selcuk University. Forty-two different test patterns were extracted from these records. These patterns were tested with the most appropriate ANN structures of single classification case and mixed classification cases. The average error of single classifications was found to be 4.3% and the average error of mixed classification 2.2%.en_US
dc.description.sponsorshipNatl Sci Fdn, TUBITAK, Sci & Tech Res Ctr Turkey, ISIK Univ, COMNET, EREL Techno Grp, GUZEL SANATLAR Printinghouse, JOHNSON&JOHNSON Med, PFIZER, SIEMENS Med, TURKCELL Iletism Hizmetler A S, ALSTOM Elect Ltd Co, GANTEK Technol & SUN Microsyst, TURCOM Co Grpen_US
dc.identifier.citationÖzbay, Y., Karlık, B., (2001). A Recognition of ECG Arrhythmias Using Artificial Neural Networks. Proceedings of the 23rd Annual International Conference of the Ieee Engineering in Medicine and Biology Society, Vols 1-4: Building New Bridges at the Frontiers of Engineering and Medicine, (23), 1680-1683.
dc.identifier.endpage1683en_US
dc.identifier.isbn0-7803-7211-5
dc.identifier.issn1094-687Xen_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1680en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/17495
dc.identifier.volume23en_US
dc.identifier.wosWOS:000178871900464en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÖzbay, Yüksel
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofProceedings of the 23rd Annual International Conference of the Ieee Engineering in Medicine and Biology Society, Vols 1-4: Building New Bridges at the Frontiers of Engineering and Medicineen_US
dc.relation.ispartofseriesProceedings of Annual International Conference of the Ieee Engineering in Medicine and Biology Society
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectarrhythmia classificationen_US
dc.subjectartificial neural networksen_US
dc.subjectECGen_US
dc.subjectheart diseasesen_US
dc.titleA Recognition of Ecg Arrhythmias Using Artificial Neural Networksen_US
dc.typeConference Objecten_US

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