Local pattern transformation-based convolutional neural network for sleep stage scoring

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dc.authorid0000-0002-1432-8360en_US
dc.authorid0000-0002-8156-016Xen_US
dc.contributor.authorZan, Hasan
dc.contributor.authorYıldız, Abdulnasır
dc.date.accessioned2024-01-24T11:18:09Z
dc.date.available2024-01-24T11:18:09Z
dc.date.issued2023en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractSleep stage scoring is essential for the diagnosis and treatment of sleep disorders. However, manual sleep scoring is a tedious, time-consuming, and subjective task. Therefore, this paper proposes a novel framework based on local pattern transformation (LPT) methods and convolutional neural networks for automatic sleep stage scoring. Unlike in previous works in other fields, these methods were not employed for manual feature extraction, which requires expert knowledge and the pipeline behind it might bias results. The transformed signals were directly fed into a CNN model (called EpochNet) that can accept multiple successive epochs. The model learns features from multiple input epochs and considers inter-epoch context during classification. To evaluate and validate the effectiveness of the proposed approach, we conducted several experiments on the Sleep-EDF dataset. Four LPT methods, including One-dimensional Local Binary Pattern (1D-LBP), Local Neighbor Descriptive Pattern (LNDP), Local Gradient Pattern (LGP), and Local Neighbor Gradient Pattern (LNGP), and different polysomnography (PSG) signals were analyzed as sequence length (number of input epochs) increased from one to five. 1D-LBP and LNDP achieved similar performances, outperforming other LPT methods that are less sensitive to local variations. The best performance was achieved when an input sequence containing five epochs of PSG signals transformed by 1D-LBP was employed. The best accuracy, F1 score, and Kohen's kappa coefficient were 0.848, 0.782, and 0.790, respectively. The results showed that our approach can achieve comparable performance to other state-of-the-art methods while occupying fewer computing resources because of the compact size of EpochNet.en_US
dc.identifier.citationZan, H. ve Yıldız, A. (2023). Local pattern transformation-based convolutional neural network for sleep stage scoring. Biomedical Signal Processing and Control, 80, 1-12.en_US
dc.identifier.doi10.1016/j.bspc.2022.104275
dc.identifier.endpage12en_US
dc.identifier.issn1746-8094
dc.identifier.scopus2-s2.0-85140039524
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1746809422007297?via%3Dihub
dc.identifier.urihttps://hdl.handle.net/11468/13265
dc.identifier.volume80en_US
dc.identifier.wosWOS:000877950400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorYıldız, Abdulnasır
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofBiomedical Signal Processing and Control
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject1D-LBPen_US
dc.subjectCNNen_US
dc.subjectConvolutional neural networken_US
dc.subjectLGPen_US
dc.subjectLNDPen_US
dc.subjectLNGPen_US
dc.subjectLocal gradient patternen_US
dc.subjectLocal neighbor descriptive patternen_US
dc.subjectLocal neighbor gradient patternen_US
dc.subjectLocal pattern transformationen_US
dc.subjectLPTen_US
dc.subjectOne-dimensional local binary patternen_US
dc.subjectSleep stage scoringen_US
dc.titleLocal pattern transformation-based convolutional neural network for sleep stage scoringen_US
dc.titleLocal pattern transformation-based convolutional neural network for sleep stage scoring
dc.typeArticleen_US

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