Publication: Morphologic based feature extraction for arrhythmia beat detection [Aritmi vuru tespiti için morfolojik tabanli öznitelik çikarma]
| dc.contributor.authors | Basar M.D., Kotan S., Kilic N., Akan A. | |
| dc.date.accessioned | 2022-03-15T02:12:45Z | |
| dc.date.accessioned | 2026-01-11T15:38:39Z | |
| dc.date.available | 2022-03-15T02:12:45Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | Heart disease is one of the diseases which has highest mortality rate recently. Heart's electrical activity examination and interpretation are very important for the understanding of diseases. In this study, electrocardiogram signals are analyzed, then patient's healthy and arrhythmia beats are extracted. RR, QRS, Skewness and Linear Predictive Coding coefficients of the signals are considered for classification of the data. K-NN, Random SubSpaces, Naive Bayes and K-Star classifiers are used. The highest accuracy is obtained with the K-NN algorithm (98.32%). At the second stage of the K-NN algorithm, accuracy levels are examined by changing the 'k' parameter. © 2016 IEEE. | |
| dc.identifier.doi | 10.1109/TIPTEKNO.2016.7863065 | |
| dc.identifier.isbn | 9781509023868 | |
| dc.identifier.uri | https://hdl.handle.net/11424/247821 | |
| dc.language.iso | tur | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | 2016 Medical Technologies National Conference, TIPTEKNO 2016 | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.subject | ECG | |
| dc.subject | feature extraction | |
| dc.subject | heart disease | |
| dc.subject | machine learning | |
| dc.title | Morphologic based feature extraction for arrhythmia beat detection [Aritmi vuru tespiti için morfolojik tabanli öznitelik çikarma] | |
| dc.type | conferenceObject | |
| dspace.entity.type | Publication | |
| oaire.citation.title | 2016 Medical Technologies National Conference, TIPTEKNO 2016 |