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dc.contributor.author강경태-
dc.date.accessioned2018-12-19T01:36:49Z-
dc.date.available2018-12-19T01:36:49Z-
dc.date.issued2018-02-
dc.identifier.citationSOFT COMPUTING, v. 22, No. 4, Page. 1225-1236en_US
dc.identifier.issn1432-7643-
dc.identifier.urihttps://link.springer.com/article/10.1007%2Fs00500-016-2410-9-
dc.identifier.urihttp://repository.hanyang.ac.kr/handle/20.500.11754/80935-
dc.description.abstractAutomatic interpretation of electrocardiograms provides a noninvasive and inexpensive technique for analyzing the heart activity of patients with a range of cardiac conditions. We propose a method that combines locally weighted linear regression with nearest neighbor search for heartbeat detection and classification in the management of non-life-threatening arrhythmia. In the proposed method, heartbeats are detected and their features are found using the Pan-Tompkins algorithm; then, they are classified by locally weighted linear regression on their nearest neighbors in a training set. The results of evaluation on data from the MIT-BIH arrhythmia database indicate that the proposed method has a sensitivity of 93.68 %, a positive predictive value of 96.62 %, and an accuracy of 98.07 % for type-oriented evaluation; and a sensitivity of 74.15 %, a positive predictive value of 72.5 %, and an accuracy of 88.69 % for patient-oriented evaluation. These results are comparable to those from existing search schemes and contribute to the systematic design of automatic heartbeat classification systems for clinical decision support.en_US
dc.description.sponsorshipThis work was partly supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H8501-16-1018)) supervised by the IITP (Institute for Information & communications Technology Promotion), and partly supported by IITP grant funded by the Korea government (MSIP) (No. B0101-15-0557, Resilient Cyber-Physical Systems Research).en_US
dc.language.isoen_USen_US
dc.publisherSPRINGERen_US
dc.subjectHeartbeat classificationen_US
dc.subjectElectrocardiogram monitoringen_US
dc.subjectLocally weighted linear regressionen_US
dc.subjectNearest neighbor searchen_US
dc.titleNearest neighbor search with locally weighted linear regression for heartbeat classificationen_US
dc.typeArticleen_US
dc.relation.no4-
dc.relation.volume22-
dc.identifier.doi10.1007/s00500-016-2410-9-
dc.relation.page1225-1236-
dc.relation.journalSOFT COMPUTING-
dc.contributor.googleauthorPark, Juyoung-
dc.contributor.googleauthorBhuiyan, Md Zakirul Alam-
dc.contributor.googleauthorKang, Mingon-
dc.contributor.googleauthorSon, Junggab-
dc.contributor.googleauthorKang, Kyungtae-
dc.relation.code2018005831-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF COMPUTING[E]-
dc.sector.departmentDIVISION OF COMPUTER SCIENCE-
dc.identifier.pidktkang-
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COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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