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

Basit Öğe Kaydı
dc.authorid0000-0002-8156-016Xen_US
dc.authorid0000-0002-1432-8360en_US
dc.contributor.authorZan, Hasan
dc.contributor.authorYıldız, Abdulnasır
dc.date.accessioned2024-02-22T12:29:33Z
dc.date.available2024-02-22T12:29:33Z
dc.date.issued2023en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği 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. ve Yıldız, A. (2018). Multi-task learning for arousal and sleep stage detection using fully convolutional networks. Journal of Neural Engineering, 20(5), 1-19.en_US
dc.identifier.doi10.1088/1741-2552/acfe3aen_US
dc.identifier.endpage19en_US
dc.identifier.issn1741-2552
dc.identifier.issue5en_US
dc.identifier.pmid37769664en_US
dc.identifier.scopus2-s2.0-85173338812en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://iopscience.iop.org/article/10.1088/1741-2552/acfe3a
dc.identifier.urihttps://hdl.handle.net/11468/13362
dc.identifier.volume20en_US
dc.identifier.wosWOS:001078082500001
dc.identifier.wosWOS:001078082500001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorYıldız, Abdulnasır
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.subjectFully convolutional networksen_US
dc.subjectMulti-ethnic study of atherosclerosis (MESA)en_US
dc.subjectMulti-task learningen_US
dc.subjectSleep arousal detectionen_US
dc.subjectSleep heart health study (SHHS)en_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

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
Multi-task learning for arousal and sleep stage detection using fully convolutional networks.pdf
Boyut:
1.65 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale Dosyası
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu