Multi-task learning for arousal and sleep stage detection using fully convolutional networks

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dc.authorid0000-0002-8156-016Xen_US
dc.contributor.authorZan, Hasan
dc.contributor.authorYıldız, Abdulnasir
dc.date.accessioned2023-10-04T11:29:38Z
dc.date.available2023-10-04T11:29:38Z
dc.date.issued2023en_US
dc.departmentMAÜ, Meslek Yüksekokulları, Mardin Meslek Yüksekokulu, Elektrik ve Enerji Bölümüen_US
dc.description.abstractObjective: Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts. Approach: In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions. Main results: By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter. Significance: Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.en_US
dc.identifier.citationZan H, Yildiz A. Multi-task learning for arousal and sleep stage detection using fully convolutional networks. J Neural Eng. 2023 Sep 28. doi: 10.1088/1741-2552/acfe3a. Epub ahead of print. PMID: 37769664.en_US
dc.identifier.doi10.1088/1741-2552/acfe3aen_US
dc.identifier.issn1741-2552
dc.identifier.pmid37769664en_US
dc.identifier.scopus2-s2.0-85173338812en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1088/1741-2552/acfe3a.
dc.identifier.urihttps://hdl.handle.net/20.500.12514/4273
dc.identifier.wosWOS:001078082500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorZan, Hasan
dc.language.isoenen_US
dc.publisherIOP Publishingen_US
dc.relation.ispartofJournal of Neural Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEEG signalsen_US
dc.subjectFully convolutional networksen_US
dc.subjectMulti-Ethnic Study of Atherosclerosisen_US
dc.subjectSleep arousal detectionen_US
dc.subjectSleep scoringen_US
dc.subjectSleep stage classificationen_US
dc.titleMulti-task learning for arousal and sleep stage detection using fully convolutional networksen_US
dc.typeArticleen_US

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