Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 강경태 | - |
dc.date.accessioned | 2018-12-19T01:36:49Z | - |
dc.date.available | 2018-12-19T01:36:49Z | - |
dc.date.issued | 2018-02 | - |
dc.identifier.citation | SOFT COMPUTING, v. 22, No. 4, Page. 1225-1236 | en_US |
dc.identifier.issn | 1432-7643 | - |
dc.identifier.uri | https://link.springer.com/article/10.1007%2Fs00500-016-2410-9 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/80935 | - |
dc.description.abstract | Automatic 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.sponsorship | This 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.iso | en_US | en_US |
dc.publisher | SPRINGER | en_US |
dc.subject | Heartbeat classification | en_US |
dc.subject | Electrocardiogram monitoring | en_US |
dc.subject | Locally weighted linear regression | en_US |
dc.subject | Nearest neighbor search | en_US |
dc.title | Nearest neighbor search with locally weighted linear regression for heartbeat classification | en_US |
dc.type | Article | en_US |
dc.relation.no | 4 | - |
dc.relation.volume | 22 | - |
dc.identifier.doi | 10.1007/s00500-016-2410-9 | - |
dc.relation.page | 1225-1236 | - |
dc.relation.journal | SOFT COMPUTING | - |
dc.contributor.googleauthor | Park, Juyoung | - |
dc.contributor.googleauthor | Bhuiyan, Md Zakirul Alam | - |
dc.contributor.googleauthor | Kang, Mingon | - |
dc.contributor.googleauthor | Son, Junggab | - |
dc.contributor.googleauthor | Kang, Kyungtae | - |
dc.relation.code | 2018005831 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF COMPUTING[E] | - |
dc.sector.department | DIVISION OF COMPUTER SCIENCE | - |
dc.identifier.pid | ktkang | - |
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