Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 강경태 | - |
dc.date.accessioned | 2018-05-29T06:56:46Z | - |
dc.date.available | 2018-05-29T06:56:46Z | - |
dc.date.issued | 2017-01 | - |
dc.identifier.citation | JOURNAL OF MEDICAL SYSTEMS, v. 41, No. 1, Article no. 11 | en_US |
dc.identifier.issn | 0148-5598 | - |
dc.identifier.issn | 1573-689X | - |
dc.identifier.uri | https://link.springer.com/article/10.1007/s10916-016-0660-9 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/71651 | - |
dc.description.abstract | Detecting arrhythmia from ECG data is now feasible on mobile devices, but in this environment it is necessary to trade computational efficiency against accuracy. We propose an adaptive strategy for feature extraction that only considers normalized beat morphology features when running in a resource-constrained environment; but in a high-performance environment it takes account of a wider range of ECG features. This process is augmented by a cascaded random forest classifier. Experiments on data from the MIT-BIH Arrhythmia Database showed classification accuracies from 96.59% to 98.51%, which are comparable to state-of-the art methods. | en_US |
dc.description.sponsorship | This work was supported by the Ministry of Science, ICT and Future Planning (MSIP), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2016-H8501-16-1018) supervised by the Institute for Information & communications Technology Promotion (IITP), and by an 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 | ECG | en_US |
dc.subject | Heartbeat classification | en_US |
dc.subject | Heartbeat morphology features | en_US |
dc.subject | Cascaded classifiers | en_US |
dc.subject | Adaptive feature extraction | en_US |
dc.subject | HEARTBEAT CLASSIFICATION | en_US |
dc.subject | NEURAL-NETWORK | en_US |
dc.subject | AUTOMATIC CLASSIFICATION | en_US |
dc.subject | ECG SIGNALS | en_US |
dc.subject | DATABASE | en_US |
dc.subject | SYSTEM | en_US |
dc.subject | RECOGNITION | en_US |
dc.subject | SMARTPHONE | en_US |
dc.title | Cascade Classification with Adaptive Feature Extraction for Arrhythmia Detection | en_US |
dc.type | Article | en_US |
dc.relation.no | 1 | - |
dc.relation.volume | 41 | - |
dc.identifier.doi | 10.1007/s10916-016-0660-9 | - |
dc.relation.page | 1-12 | - |
dc.relation.journal | JOURNAL OF MEDICAL SYSTEMS | - |
dc.contributor.googleauthor | Park, Juyoung | - |
dc.contributor.googleauthor | Kang, Mingon | - |
dc.contributor.googleauthor | Gao, Jean | - |
dc.contributor.googleauthor | Kim, Younghoon | - |
dc.contributor.googleauthor | Kang, Kyungtae | - |
dc.relation.code | 2017006549 | - |
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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