Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Cho, Jae Hoon | - |
dc.contributor.author | Choi, Ji Ho | - |
dc.contributor.author | Moon, Ji Eun | - |
dc.contributor.author | Lee, Young Jun | - |
dc.contributor.author | Lee, Ho Dong | - |
dc.contributor.author | Ha, Tae Kyoung | - |
dc.date.accessioned | 2022-07-05T01:40:18Z | - |
dc.date.available | 2022-07-05T01:40:18Z | - |
dc.date.issued | 2022-06 | - |
dc.identifier.issn | 1010-660X | - |
dc.identifier.issn | 1648-9144 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/21129 | - |
dc.description.abstract | Background and Objectives: Polysomnography is manually scored by sleep experts. However, manual scoring is a time-consuming and labor-intensive task. The goal of this study was to verify the accuracy of automated sleep-stage scoring based on a deep learning algorithm compared to manual sleep-stage scoring. Materials and Methods: A total of 602 polysomnography datasets from subjects (Male:Female = 397:205) aged 19 to 65 years (mean age, 43.8, standard deviation = 12.2) were included in the study. The performance of the proposed model was evaluated based on kappa value and bootstrapped point-estimate of median percent agreement with a 95% bootstrap confidence interval and R = 1000. The proposed model was trained using 482 datasets and validated using 48 datasets. For testing, 72 datasets were selected randomly. Results: The proposed model exhibited good concordance rates with manual scoring for stages W (94%), N1 (83.9%), N2 (89%), N3 (92%), and R (93%). The average kappa value was 0.84. For the bootstrap method, high overall agreement between the automated deep learning algorithm and manual scoring was observed in stages W (98%), N1 (94%), N2 (92%), N3 (99%), and R (98%) and total (96%). Conclusions: Automated sleep-stage scoring using the proposed model may be a reliable method for sleep-stage classification. | - |
dc.format.extent | 8 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/medicina58060779 | - |
dc.identifier.scopusid | 2-s2.0-85132745861 | - |
dc.identifier.wosid | 000816555100001 | - |
dc.identifier.bibliographicCitation | Medicina (Kaunas, Lithuania), v.58, no.6, pp 1 - 8 | - |
dc.citation.title | Medicina (Kaunas, Lithuania) | - |
dc.citation.volume | 58 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 8 | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | General & Internal Medicine | - |
dc.relation.journalWebOfScienceCategory | Medicine, General & Internal | - |
dc.subject.keywordPlus | INTERRATER RELIABILITY | - |
dc.subject.keywordPlus | HEALTH OUTCOMES | - |
dc.subject.keywordPlus | METAANALYSIS | - |
dc.subject.keywordPlus | DURATION | - |
dc.subject.keywordPlus | EEG | - |
dc.subject.keywordAuthor | polysomnography | - |
dc.subject.keywordAuthor | sleep stages | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | algorithms | - |
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