{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:06:36Z","timestamp":1792793196186,"version":"4.5.0"},"reference-count":59,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:00:00Z","timestamp":1741651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T00:00:00Z","timestamp":1741651200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62271293"],"award-info":[{"award-number":["62271293"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"crossref","award":["ZR2022MF289"],"award-info":[{"award-number":["ZR2022MF289"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Study on the Effect of Sleep Disturbance Intervention on Learning and Memory in AD and Its Molecular and Pathological Mechanisms","award":["ZR2020MH160"],"award-info":[{"award-number":["ZR2020MH160"]}]},{"name":"Colleges and Universities Youth Talent Promotion Program of Shandong Province"},{"name":"Research Leader Program of Jinan Science and Technology Bureau","award":["2019GXRC061"],"award-info":[{"award-number":["2019GXRC061"]}]},{"name":"Introduce Innovative Teams of 2021","award":["2021GXRC071"],"award-info":[{"award-number":["2021GXRC071"]}]},{"name":"Innovation Ability Enhancement Project for Shandong Province Science and Technology Small and Medium Enterprises","award":["2023TSGC0255"],"award-info":[{"award-number":["2023TSGC0255"]}]},{"name":"Qilu University of Technology(Shandong Academy of Sciences) Basic Research Project of Science, Education and Industry Integration Pilot","award":["2023PY046"],"award-info":[{"award-number":["2023PY046"]}],"id":[{"id":"https:\/\/ror.org\/04y8d6y55","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Talent Training and Teaching Reform Project of Qilu University of Technology in 2022","award":["P202204"],"award-info":[{"award-number":["P202204"]}],"id":[{"id":"https:\/\/ror.org\/04hyzq608","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Graduate Education and Teaching Reform Project of Qilu University of Technology","id":[{"id":"https:\/\/ror.org\/04hyzq608","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Major Innovation Project for the Science Education Industry Integration Pilot Project of Qilu University of Technology","award":["2023JBZ03"],"award-info":[{"award-number":["2023JBZ03"]}],"id":[{"id":"https:\/\/ror.org\/04hyzq608","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2025,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Obstructive sleep apnea (OSA) is a prevalent sleep disorder. Accurate sleep staging is one of the prerequisites in the study of sleep-related disorders and the evaluation of sleep quality. We introduce a novel GraphSleepFormer (GSF) network designed to effectively capture global dependencies and node characteristics in graph-structured data.\n                    <jats:italic>Approach.<\/jats:italic>\n                    The network incorporates centrality coding and spatial coding into its architecture. It employs adaptive learning of adjacency matrices for spatial encoding between channels located on the head, thereby encoding graph structure information to enhance the model\u2019s representation and understanding of spatial relationships. Centrality encoding integrates the degree matrix into node features, assigning varying degrees of attention to different channels. Ablation experiments demonstrate the effectiveness of these encoding methods. The Shapley Additive Explanations (SHAP) method was employed to evaluate the contribution of each channel in sleep staging, highlighting the necessity of using multimodal data.\n                    <jats:italic>Main results.<\/jats:italic>\n                    We trained our model on overnight polysomnography data collected from 28 OSA patients in a clinical setting and achieved an overall accuracy of 80.10%. GSF achieved performance comparable to state-of-the-art methods on two subsets of the ISRUC database.\n                    <jats:italic>Significance.<\/jats:italic>\n                    The GSF Accurately identifies sleep periods, providing a critical basis for diagnosing and treating OSA, thereby contributing to advancements in sleep medicine.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/adb996","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T17:49:50Z","timestamp":1740419390000},"page":"026011","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["GraphSleepFormer: a multi-modal graph neural network for sleep staging in OSA patients"],"prefix":"10.1088","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7675-2851","authenticated-orcid":false,"given":"Chen","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9779-3026","authenticated-orcid":false,"given":"Xiuquan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5585-0866","authenticated-orcid":false,"given":"Chengyan","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8016-0624","authenticated-orcid":false,"given":"Qi","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3290-4209","authenticated-orcid":false,"given":"Pengcheng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8274-1548","authenticated-orcid":false,"given":"Di","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6102-6832","authenticated-orcid":true,"given":"Chao","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7660-1206","authenticated-orcid":true,"given":"Fangzhou","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1469-3488","authenticated-orcid":false,"given":"Shanshan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8377-2166","authenticated-orcid":false,"given":"Tzyy-Ping","family":"Jung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2427-7499","authenticated-orcid":true,"given":"Jiancai","family":"Leng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"jneadb996bib1","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.ncl.2005.05.005","article-title":"Sleep-disordered breathing and cerebrovascular disease: a mechanistic approach","volume":"23","author":"Lavie","year":"2005","journal-title":"Neurol. Clin."},{"key":"jneadb996bib2","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1053\/smrv.2002.0261","article-title":"Obstructive sleep apnoea syndrome\u2013an oxidative stress disorder","volume":"7","author":"Lavie","year":"2003","journal-title":"Sleep Med. Rev."},{"key":"jneadb996bib3","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/S2213-2600(19)30198-5","article-title":"Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis","volume":"7","author":"Benjafield","year":"2019","journal-title":"Lancet Respir. Med."},{"key":"jneadb996bib4","doi-asserted-by":"publisher","first-page":"597","DOI":"10.5664\/jcsm.2172","article-title":"Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. Deliberations of the sleep apnea definitions task force of the American Academy of Sleep Medicine","volume":"8","author":"Berry","year":"2012","journal-title":"J. Clin. Sleep Med."},{"key":"jneadb996bib5","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1007\/s00521-012-1065-4","article-title":"Classification of sleep stages using class-dependent sequential feature selection and artificial neural network","volume":"23","author":"\u00d6z\u015fen","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"jneadb996bib6","doi-asserted-by":"publisher","DOI":"10.1111\/jsr.12786","article-title":"Machine\u2010learning\u2010derived sleep\u2013wake staging from around\u2010the\u2010ear electroencephalogram outperforms manual scoring and actigraphy","volume":"28","author":"Mikkelsen","year":"2019","journal-title":"J. Sleep Res."},{"key":"jneadb996bib7","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/B0-72-160797-7\/50009-4","article-title":"Normal human sleep: an overview","volume":"4","author":"Carskadon","year":"2005","journal-title":"Princ. Pract. sleep Med."},{"key":"jneadb996bib8","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.ccm.2010.02.005","article-title":"Polysomnography","volume":"31","author":"Jafari","year":"2010","journal-title":"Clin. Chest Med."},{"key":"jneadb996bib9","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1109\/TNSRE.2018.2813138","article-title":"A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series","volume":"26","author":"Chambon","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneadb996bib10","first-page":"1452","article-title":"Automatic sleep stage classification using single-channel eeg: learning sequential features with attention-based recurrent neural networks","author":"Phan","year":"2018"},{"key":"jneadb996bib11","first-page":"1324","article-title":"GraphSleepNet: adaptive spatial-temporal graph convolutional networks for sleep stage classification","author":"Jia","year":"2020"},{"key":"jneadb996bib12","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1109\/TAI.2021.3060350","article-title":"SleepPrintNet: a multivariate multimodal neural network based on physiological time-series for automatic sleep staging","volume":"1","author":"Jia","year":"2020","journal-title":"IEEE Trans. Artif. Intell."},{"key":"jneadb996bib13","doi-asserted-by":"publisher","first-page":"2073","DOI":"10.1109\/JBHI.2019.2951346","article-title":"Accurate deep learning-based sleep staging in a clinical population with suspected obstructive sleep apnea","volume":"24","author":"Korkalainen","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"jneadb996bib14","doi-asserted-by":"publisher","first-page":"2456","DOI":"10.1109\/TBME.2022.3147187","article-title":"SleepTransformer: automatic sleep staging with interpretability and uncertainty quantification","volume":"69","author":"Phan","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"jneadb996bib15","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"jneadb996bib16","doi-asserted-by":"publisher","first-page":"60588","DOI":"10.1109\/ACCESS.2021.3071274","article-title":"Graph neural network: a comprehensive review on non-euclidean space","volume":"9","author":"Asif","year":"2021","journal-title":"IEEE Access"},{"key":"jneadb996bib17","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/TNSRE.2022.3176004","article-title":"Jumping knowledge based spatial-temporal graph convolutional networks for automatic sleep stage classification","volume":"30","author":"Ji","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneadb996bib18","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.1088116","article-title":"Exploration of sleep function connection and classification strategies based on sub-period sleep stages","volume":"16","author":"Xu","year":"2023","journal-title":"Front. Neurosci."},{"key":"jneadb996bib19","doi-asserted-by":"publisher","first-page":"1977","DOI":"10.1109\/TNSRE.2021.3110665","article-title":"Multi-view spatial-temporal graph convolutional networks with domain generalization for sleep stage classification","volume":"29","author":"Jia","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneadb996bib20","doi-asserted-by":"publisher","first-page":"6000","DOI":"10.5555\/3295222.3295349","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"jneadb996bib21","first-page":"4774","article-title":"State-of-the-art speech recognition with sequence-to-sequence models","author":"Chiu","year":"2018"},{"key":"jneadb996bib22","first-page":"28877","article-title":"Do transformers really perform badly for graph representation?","author":"Ying","year":"2021"},{"key":"jneadb996bib23","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.1007\/s11325-024-03025-0","article-title":"Validation of self-applied unattended polysomnography using somte V2 PSG (somte) for diagnosis of obstructive sleep apnoea (OSA) in pregnant women in early to mid-gestation","volume":"28","author":"Clements","year":"2024","journal-title":"Sleep Breath."},{"key":"jneadb996bib24","doi-asserted-by":"publisher","first-page":"757","DOI":"10.5664\/jcsm.5812","article-title":"Updated adaptive servo-ventilation recommendations for the 2012 AASM guideline:\u201cThe treatment of central sleep apnea syndromes in adults: practice parameters with an evidence-based literature review and meta-analyses*","volume":"12","author":"Aurora","year":"2016","journal-title":"J. Clin. Sleep Med."},{"key":"jneadb996bib25","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.cmpb.2015.10.013","article-title":"ISRUC-sleep: a comprehensive public dataset for sleep researchers","volume":"124","author":"Khalighi","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"jneadb996bib26","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.cmpb.2007.04.004","article-title":"Digital butterworth filter for subtracting noise from low magnitude surface electromyogram","volume":"87","author":"Mello","year":"2007","journal-title":"Comput. Methods Programs Biomed."},{"key":"jneadb996bib27","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1109\/TBME.2003.814293","article-title":"A resampling method for estimating the signal subspace of spatio-temporal EEG\/MEG data","volume":"50","author":"Maris","year":"2003","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"jneadb996bib28","doi-asserted-by":"publisher","first-page":"1998","DOI":"10.1109\/TNSRE.2017.2721116","article-title":"DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG","volume":"25","author":"Supratak","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneadb996bib29","first-page":"2319","article-title":"Feature extraction using convolution neural networks (CNN) and deep learning","author":"Jogin","year":"2018"},{"key":"jneadb996bib30","first-page":"3412","article-title":"Convolutional RNN: an enhanced model for extracting features from sequential data","author":"Keren","year":"2016"},{"key":"jneadb996bib31","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.future.2020.08.005","article-title":"ABCDM: an attention-based bidirectional CNN-RNN deep model for sentiment analysis","volume":"115","author":"Basiri","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"jneadb996bib32","doi-asserted-by":"publisher","first-page":"142","DOI":"10.5935\/1984-0063.20200056","article-title":"A comprehensive review of obstructive sleep apnea","volume":"14","author":"Abbasi","year":"2021","journal-title":"Sleep Sci."},{"key":"jneadb996bib33","doi-asserted-by":"publisher","first-page":"09TR01","DOI":"10.1088\/1361-6579\/aad5fe","article-title":"Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity","volume":"39","author":"Mazzotti","year":"2018","journal-title":"Physiol. Meas."},{"key":"jneadb996bib34","doi-asserted-by":"publisher","first-page":"2927","DOI":"10.1109\/TNSRE.2022.3213848","article-title":"Adaptive margin aware complement-cross entropy loss for improving class imbalance in multi-view sleep staging based on EEG signals","volume":"30","author":"Miao","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneadb996bib35","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2023.1188678","article-title":"SeriesSleepNet: an EEG time series model with partial data augmentation for automatic sleep stage scoring","volume":"14","author":"Lee","year":"2023","journal-title":"Front. Physiol."},{"key":"jneadb996bib36","doi-asserted-by":"publisher","first-page":"2764","DOI":"10.1016\/j.procs.2020.09.280","article-title":"A deep learning approach to detect sleep stages","volume":"176","author":"Stuburi\u0107","year":"2020","journal-title":"Proc. Comput. Sci."},{"key":"jneadb996bib37","article-title":"Grpe: relative positional encoding for graph transformer","author":"Park","year":"2022"},{"key":"jneadb996bib38","doi-asserted-by":"publisher","first-page":"899","DOI":"10.3390\/s23020899","article-title":"Influence of channel selection and subject\u2019s age on the performance of the single channel EEG-based automatic sleep staging algorithms","volume":"23","author":"Nazih","year":"2023","journal-title":"Sensors"},{"key":"jneadb996bib39","article-title":"Gophormer: ego-graph transformer for node classification","author":"Zhao","year":"2021"},{"key":"jneadb996bib40","doi-asserted-by":"publisher","first-page":"2224","DOI":"10.5555\/2969442.2969488","article-title":"Convolutional networks on graphs for learning molecular fingerprints","volume":"vol 2","author":"Duvenaud","year":"2015"},{"key":"jneadb996bib41","article-title":"Deep convolutional networks on graph-structured data","author":"Henaff","year":"2015"},{"key":"jneadb996bib42","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102037","article-title":"Intra-and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG","volume":"61","author":"Seo","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"jneadb996bib43","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/978-3-030-74361-1_7","article-title":"Matched-pairs tests","author":"Berry","year":"2021"},{"key":"jneadb996bib44","doi-asserted-by":"publisher","first-page":"2839","DOI":"10.1016\/j.patcog.2015.03.009","article-title":"Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation","volume":"48","author":"Wong","year":"2015","journal-title":"Pattern Recognit."},{"key":"jneadb996bib45","first-page":"1","article-title":"Evaluation of osa patient sleep stage classification performance using a multi-channel psg dataset","author":"Na","year":"2022"},{"key":"jneadb996bib46","first-page":"5535","article-title":"Relationship between sleep stages and HRV response in obstructive sleep apnea patients","author":"Romero","year":"2021"},{"key":"jneadb996bib47","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1109\/TIM.2018.2799059","article-title":"Ensemble SVM method for automatic sleep stage classification","volume":"67","author":"Alickovic","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"jneadb996bib48","first-page":"4920","article-title":"A new fully automated random-forest algorithm for sleep staging","author":"Klok","year":"2018"},{"key":"jneadb996bib49","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.compbiomed.2019.01.013","article-title":"Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals","volume":"106","author":"Michielli","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"jneadb996bib50","first-page":"78","article-title":"Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification","author":"Yadav","year":"2016"},{"key":"jneadb996bib51","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.future.2020.03.019","article-title":"Sleep staging by bidirectional long short-term memory convolution neural network","volume":"109","author":"Chen","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"jneadb996bib52","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106676","article-title":"An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals","volume":"155","author":"Khare","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"jneadb996bib53","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1109\/JBHI.2021.3099295","article-title":"Estimation of sleep stages analyzing respiratory and movement signals","volume":"26","author":"Gaiduk","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"jneadb996bib54","doi-asserted-by":"publisher","first-page":"2930","DOI":"10.1016\/j.procs.2022.09.351","article-title":"Explainable machine learning for sleep apnea prediction","volume":"207","author":"Troncoso-Garc\u00eda","year":"2022","journal-title":"Proc. Comput. Sci."},{"key":"jneadb996bib55","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107925","article-title":"Validation and interpretation of a multimodal drowsiness detection system using explainable machine learning","volume":"243","author":"Hasan","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"key":"jneadb996bib56","doi-asserted-by":"publisher","DOI":"10.1111\/jsr.12977","article-title":"Sex differences in brain oscillatory activity during sleep and wakefulness in obstructive sleep apnea","volume":"29","author":"Mu\u00f1oz\u2010Torres","year":"2020","journal-title":"J. Sleep Res."},{"key":"jneadb996bib57","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.2147\/NSS.S327110","article-title":"Deep learning\u2013based assessment of brain connectivity related to obstructive sleep apnea and daytime sleepiness","volume":"13","author":"Lee","year":"2021","journal-title":"Nat. Sci. Sleep"},{"key":"jneadb996bib58","doi-asserted-by":"publisher","first-page":"5199","DOI":"10.3390\/ijerph19095199","article-title":"CAttSleepNet: automatic end-to-end sleep staging using attention-based deep neural networks on single-channel EEG","volume":"19","author":"Li","year":"2022","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"jneadb996bib59","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108783","article-title":"Toward sleep apnea detection with lightweight multi-scaled fusion network","volume":"247","author":"Chen","year":"2022","journal-title":"Knowl.-Based Syst."}],"container-title":["Journal of Neural Engineering"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adb996","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adb996\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adb996","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adb996\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adb996\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adb996\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adb996\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adb996\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T09:29:30Z","timestamp":1741685370000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/adb996"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,11]]},"references-count":59,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,3,11]]},"published-print":{"date-parts":[[2025,4,1]]}},"URL":"https:\/\/doi.org\/10.1088\/1741-2552\/adb996","relation":{},"ISSN":["1741-2560","1741-2552"],"issn-type":[{"value":"1741-2560","type":"print"},{"value":"1741-2552","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,11]]},"assertion":[{"value":"GraphSleepFormer: a multi-modal graph neural network for sleep staging in OSA patients","name":"article_title","label":"Article Title"},{"value":"Journal of Neural Engineering","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2024-10-14","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-02-24","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-03-11","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}