{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:16:46Z","timestamp":1771024606639,"version":"3.50.1"},"reference-count":34,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2024,8,5]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Sleep arousal detection is an important factor to monitor the sleep disorder.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>Thus, a unique <jats:italic>n<\/jats:italic>th layer one-dimensional (1D) convolutional neural network-based U-Net model for automatic sleep arousal identification has been proposed.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The proposed method has achieved area under the precision\u2013recall curve performance score of 0.498 and area under the receiver operating characteristics performance score of 0.946.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>No other researchers have suggested U-Net-based detection of sleep arousal.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>From the experimental results, it has been found that U-Net performs better accuracy as compared to the state-of-the-art methods.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>Sleep arousal detection is an important factor to monitor the sleep disorder. Objective of the work is to detect the sleep arousal using different physiological channels of human body.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Social implications<\/jats:title><jats:p>It will help in improving mental health by monitoring a person's sleep.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-07-2023-0302","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T10:55:53Z","timestamp":1705056953000},"page":"575-589","source":"Crossref","is-referenced-by-count":5,"title":["Sleep arousal detection for monitoring of sleep disorders using one-dimensional convolutional neural network-based U-Net and bio-signals"],"prefix":"10.1108","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1078-3294","authenticated-orcid":false,"given":"Priya","family":"Mishra","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8270-2927","authenticated-orcid":false,"given":"Aleena","family":"Swetapadma","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"key2025010616354884200_ref001","doi-asserted-by":"publisher","first-page":"3","DOI":"10.22489\/CinC.2018.245","article-title":"SleepTight: identifying sleep arousals using inter and intra-relation of multimodal signals","volume":"45","year":"2018","journal-title":"Computing in Cardiology"},{"key":"key2025010616354884200_ref003","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1093\/sleep\/15.2.173","article-title":"EEG arousals: scoring rules and examples. 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