Identification of Breathing Patterns through EEG Signal Analysis Using Machine Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hong, Yong-Gi | - |
dc.contributor.author | Kim, Hang-Keun | - |
dc.contributor.author | Son, Young-Don | - |
dc.contributor.author | Kang, Chang-Ki | - |
dc.date.available | 2021-04-14T00:40:24Z | - |
dc.date.created | 2021-03-19 | - |
dc.date.issued | 2021-03 | - |
dc.identifier.issn | 2076-3425 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80727 | - |
dc.description.abstract | This study was to investigate the changes in brain function due to lack of oxygen (O2) caused by mouth breathing, and to suggest a method to alleviate the side effects of mouth breathing on brain function through an additional O2 supply. For this purpose, we classified the breathing patterns according to EEG signals using a machine learning technique and proposed a method to reduce the side effects of mouth breathing on brain function. Twenty subjects participated in this study, and each subject performed three different breathings: nose and mouth breathing and mouth breathing with O2 supply during a working memory task. The results showed that nose breathing guarantees normal O2 supply to the brain, but mouth breathing interrupts the O2 supply to the brain. Therefore, this comparative study of EEG signals using machine learning showed that one of the most important elements distinguishing the effects of mouth and nose breathing on brain function was the difference in O2 supply. These findings have important implications for the workplace en-vironment, suggesting that special care is required for employees who work long hours in confined spaces such as public transport, and that a sufficient O2 supply is needed in the workplace for working efficiency. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | Brain Sciences | - |
dc.title | Identification of Breathing Patterns through EEG Signal Analysis Using Machine Learning | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000633422700001 | - |
dc.identifier.doi | 10.3390/brainsci11030293 | - |
dc.identifier.bibliographicCitation | Brain Sciences, v.11, no.3 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85102376760 | - |
dc.citation.title | Brain Sciences | - |
dc.citation.volume | 11 | - |
dc.citation.number | 3 | - |
dc.contributor.affiliatedAuthor | Hong, Yong-Gi | - |
dc.contributor.affiliatedAuthor | Kim, Hang-Keun | - |
dc.contributor.affiliatedAuthor | Son, Young-Don | - |
dc.contributor.affiliatedAuthor | Kang, Chang-Ki | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Breathing | - |
dc.subject.keywordAuthor | EEG | - |
dc.subject.keywordAuthor | LDA | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Random forest | - |
dc.subject.keywordAuthor | Working memory task | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon 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.