FitMine: automatic mining for time-evolving signals of cardiotocography monitoring
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Sun-Hee | - |
dc.contributor.author | Yang, Hyung-Jeong | - |
dc.contributor.author | Lee, Seong-Whan | - |
dc.date.accessioned | 2021-09-03T04:29:07Z | - |
dc.date.available | 2021-09-03T04:29:07Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2017-07 | - |
dc.identifier.issn | 1384-5810 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/82966 | - |
dc.description.abstract | The monitoring and assessment of the fetus condition are considered to be among the most important obstetric issues to consider during pregnancy and the prenatal period. Monitoring the fetal condition is required to detect the presence of any abnormalities in the oxygen supply to the fetus early in the antenatal or labor period. Early detection can prevent permanent brain damage and death, both of which may arise from suffocation caused by fetal disease, hypoxic-ischemic injury in the neonatal brain, or chronic fetal asphyxia. In this paper, we propose a new signal-fitting method, FitMine, that identifies the fetal condition by analyzing fetal heart rate (FHR) and uterine contraction (UC) signals that are non-invasively measured by cardiotocography (CTG). FitMine is a novel nonlinear dynamic model that reflects the relation between the FHR and UC signals; it combines the chaotic population model and unscented Kalman filter algorithm. The proposed method has several benefits. These are: (a) change-point detection: the proposed method can detect significant pattern variations such as high or low peaks changing suddenly in the FHR and UC signals; (b) parameter-free: it is performed automatically without the requirement for the user to enter input parameters; (c) scalability: FitMine is linearly scalable according to the size of the input data; and (d) applicability: the proposed model can be applied to detect abnormal signs in various domains including electroencephalogram data, epidemic data, temperature data, in addition to CTG recordings. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | HEART-RATE PATTERNS | - |
dc.subject | FETAL | - |
dc.subject | CLASSIFICATION | - |
dc.title | FitMine: automatic mining for time-evolving signals of cardiotocography monitoring | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sun-Hee | - |
dc.contributor.affiliatedAuthor | Lee, Seong-Whan | - |
dc.identifier.doi | 10.1007/s10618-017-0493-2 | - |
dc.identifier.scopusid | 2-s2.0-85012253425 | - |
dc.identifier.wosid | 000403702000001 | - |
dc.identifier.bibliographicCitation | DATA MINING AND KNOWLEDGE DISCOVERY, v.31, no.4, pp.909 - 933 | - |
dc.relation.isPartOf | DATA MINING AND KNOWLEDGE DISCOVERY | - |
dc.citation.title | DATA MINING AND KNOWLEDGE DISCOVERY | - |
dc.citation.volume | 31 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 909 | - |
dc.citation.endPage | 933 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | HEART-RATE PATTERNS | - |
dc.subject.keywordPlus | FETAL | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordAuthor | Fetal heart rate | - |
dc.subject.keywordAuthor | Cardiotocography | - |
dc.subject.keywordAuthor | Uterine contraction | - |
dc.subject.keywordAuthor | Chaos population model | - |
dc.subject.keywordAuthor | Parameter estimation | - |
dc.subject.keywordAuthor | Unscented Kalman filter | - |
dc.subject.keywordAuthor | Minimum description length | - |
dc.subject.keywordAuthor | Automatic mining | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.