Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 46140 | - |
dc.citation.startPage | 46131 | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 8 | - |
dc.contributor.author | Kim, Sung-Woo | - |
dc.contributor.author | Lee, Kwangmuk | - |
dc.contributor.author | Yeom, Junyeong | - |
dc.contributor.author | Lee, Tae-Hoon | - |
dc.contributor.author | Kim, Don-Han | - |
dc.contributor.author | Kim, Jae Joon | - |
dc.date.accessioned | 2023-12-21T17:50:17Z | - |
dc.date.available | 2023-12-21T17:50:17Z | - |
dc.date.created | 2020-03-07 | - |
dc.date.issued | 2020-03 | - |
dc.description.abstract | This paper presents a wearable multi-biosignal wireless interface for sleep analysis. It enables comfortable sleep monitoring with direct sleep-stage classification capability while conventional analytic interfaces including the Polysomnography (PSG) require complex post-processing analyses based on heavy raw data, need expert supervision for measurements, or do not provide comfortable fit for long-time wearing. The proposed multi-biosignal interface consists of electroencephalography (EEG), electromyography (EMG), and electrooculography (EOG). A readout integrated circuit (ROIC) is designed to collect three kinds of bio-potential signals through four internal readout channels, where their analog feature extraction circuits are included together to provide sleep-stage classification directly. The designed multi-biosignal sensing ROIC is fabricated in a 180-nm complementary metal & x2013;oxide & x2013;semiconductor (CMOS) process. For system-level verification, its low-power headband-style analytic device is implemented for wearable sleep monitoring, where the direct sleep-stage classification is performed based on a decision tree algorithm. It is functionally verified by comparison experiments with post-processing analysis results from the OpenBCI module, whose sleep-stage detection shows reasonable correlation of 74% for four sleep stages. | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.8, pp.46131 - 46140 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2978391 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.scopusid | 2-s2.0-85081998847 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/31548 | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9025032 | - |
dc.identifier.wosid | 000524575400010 | - |
dc.language | 영어 | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Wearable Multi-Biosignal Analysis Integrated Interface with Direct Sleep-Stage Classification | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications | - |
dc.relation.journalResearchArea | Computer Science; Engineering; Telecommunications | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Sleep-stage classification | - |
dc.subject.keywordAuthor | multi-biosignal interface | - |
dc.subject.keywordAuthor | rule-based decision tree | - |
dc.subject.keywordAuthor | feature extraction stage | - |
dc.subject.keywordAuthor | readout integrated circuit | - |
dc.subject.keywordAuthor | wearable device | - |
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