{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T04:13:41Z","timestamp":1772424821312,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102241"],"award-info":[{"award-number":["62102241"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Natural Science Foundation of Shanghai Municipality","doi-asserted-by":"publisher","award":["23ZR1425400"],"award-info":[{"award-number":["23ZR1425400"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Eye & ENT Hospital\u2019s double priority project A","award":["YGJC026"],"award-info":[{"award-number":["YGJC026"]}]},{"name":"Shanghai Municipal Key Clinical Specialty, China","award":["shslczdzk00801"],"award-info":[{"award-number":["shslczdzk00801"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Polysomnography is the diagnostic gold standard for obstructive sleep apnea-hypopnea syndrome (OSAHS), requiring medical professionals to analyze apnea-hypopnea events from multidimensional data throughout the sleep cycle. This complex process is susceptible to variability based on the clinician\u2019s experience, leading to potential inaccuracies. Existing automatic diagnosis methods often overlook multimodal physiological signals and medical prior knowledge, leading to limited diagnostic capabilities. This study presents a novel <jats:bold>hetero<\/jats:bold>geneous <jats:bold>g<\/jats:bold>raph <jats:bold>c<\/jats:bold>onvolutional <jats:bold>f<\/jats:bold>usion <jats:bold>net<\/jats:bold>work (<jats:bold>HeteroGCFNet<\/jats:bold>) leveraging multimodal physiological signals and domain knowledge for automated OSAHS diagnosis. This framework constructs two types of graph representations: physical space graphs, which map the spatial layout of sensors on the human body, and process knowledge graphs which detail the physiological relationships among breathing patterns, oxygen saturation, and vital signals. The framework leverages heterogeneous graph convolutional neural networks to extract both localized and global features from these graphs. Additionally, a multi-head fusion module combines these features into a unified representation for effective classification, enhancing focus on relevant signal characteristics and cross-modal interactions. This study evaluated the proposed framework on a large-scale OSAHS dataset, combined from publicly available sources and data provided by a collaborative university hospital. It demonstrated superior diagnostic performance compared to conventional machine learning models and existing deep learning approaches, effectively integrating domain knowledge with data-driven learning to produce explainable representations and robust generalization capabilities, which can potentially be utilized for clinical use. Code is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/AmbitYuki\/HeteroGCFNet\" ext-link-type=\"uri\">https:\/\/github.com\/AmbitYuki\/HeteroGCFNet<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s40747-024-01648-0","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T03:39:18Z","timestamp":1731901158000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9575-7345","authenticated-orcid":false,"given":"Haoyu","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4024-925X","authenticated-orcid":false,"given":"Xihe","family":"Qiu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1374-1524","authenticated-orcid":false,"given":"Bin","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3555-7143","authenticated-orcid":false,"given":"Xiaoyu","family":"Tan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9237-6595","authenticated-orcid":false,"given":"Jingjing","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,18]]},"reference":[{"issue":"34","key":"1648_CR1","doi-asserted-by":"crossref","first-page":"2103559","DOI":"10.1002\/adfm.202103559","volume":"31","author":"X Peng","year":"2021","unstructured":"Peng X et al (2021) All-nanofiber self-powered skin-interfaced real-time respiratory monitoring system for obstructive sleep apnea-hypopnea syndrome diagnosing. Adv Funct Mater 31(34):2103559","journal-title":"Adv Funct Mater"},{"key":"1648_CR2","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11325-019-01952-x","volume":"24","author":"C O\u2019Connor-Reina","year":"2020","unstructured":"O\u2019Connor-Reina C et al (2020) Tongue peak pressure: a tool to aid in the identification of obstruction sites in patients with obstructive sleep apnea\/hypopnea syndrome. Sleep Breathing 24:281\u2013286","journal-title":"Sleep Breathing"},{"key":"1648_CR3","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s00405-020-05948-2","volume":"278","author":"W Kong","year":"2021","unstructured":"Kong W et al (2021) Biomarkers of alzheimer\u2019s disease in severe obstructive sleep apnea-hypopnea syndrome in the chinese population. Eur Arch Oto-Rhino-Laryng 278:865\u2013872","journal-title":"Eur Arch Oto-Rhino-Laryng"},{"issue":"1","key":"1648_CR4","doi-asserted-by":"crossref","first-page":"0227778","DOI":"10.1371\/journal.pone.0227778","volume":"15","author":"V Poka-Mayap","year":"2020","unstructured":"Poka-Mayap V et al (2020) Obstructive sleep apnea and hypopnea syndrome in patients admitted in a tertiary hospital in cameroon: prevalence and associated factors. PLoS One 15(1):0227778","journal-title":"PLoS One"},{"issue":"12","key":"1648_CR5","doi-asserted-by":"crossref","first-page":"122512261","DOI":"10.21037\/apm-21-3302","volume":"10","author":"F Yuan","year":"2021","unstructured":"Yuan F et al (2021) Correlation between obstructive sleep apnea hypopnea syndrome and hypertension: a systematic review and meta-analysis. Annals of Palliative Medicine 10(12):122512261\u2013122512261","journal-title":"Annals of Palliative Medicine"},{"issue":"2","key":"1648_CR6","doi-asserted-by":"crossref","first-page":"586","DOI":"10.14336\/AD.2020.0723","volume":"12","author":"Y Li","year":"2021","unstructured":"Li Y, Wang Y (2021) Obstructive sleep apnea-hypopnea syndrome as a novel potential risk for aging. Aging Dis 12(2):586","journal-title":"Aging Dis"},{"issue":"1","key":"1648_CR7","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/TITB.2008.2004495","volume":"13","author":"AH Khandoker","year":"2008","unstructured":"Khandoker AH, Palaniswami M, Karmakar CK (2008) Support vector machines for automated recognition of obstructive sleep apnea syndrome from ecg recordings. IEEE Trans Inform Technol Biomed 13(1):37\u201348","journal-title":"IEEE Trans Inform Technol Biomed"},{"issue":"1","key":"1648_CR8","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41598-020-79217-x","volume":"11","author":"K Sundararajan","year":"2021","unstructured":"Sundararajan K et al (2021) Sleep classification from wrist-worn accelerometer data using random forests. Sci Rep 11(1):24","journal-title":"Sci Rep"},{"issue":"4","key":"1648_CR9","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1001\/archotol.130.4.453","volume":"130","author":"EM Weaver","year":"2004","unstructured":"Weaver EM, Kapur V, Yueh B (2004) Polysomnography vs self-reported measures in patients with sleep apnea. Arch Otolaryngol Head Neck Surg 130(4):453\u2013458","journal-title":"Arch Otolaryngol Head Neck Surg"},{"issue":"4","key":"1648_CR10","doi-asserted-by":"crossref","first-page":"599","DOI":"10.3390\/ijerph16040599","volume":"16","author":"O Yildirim","year":"2019","unstructured":"Yildirim O, Baloglu UB, Acharya UR (2019) A deep learning model for automated sleep stages classification using psg signals. Int J Environ Res Public Health 16(4):599","journal-title":"Int J Environ Res Public Health"},{"key":"1648_CR11","doi-asserted-by":"crossref","unstructured":"Howe-Patterson M, Pourbabaee B, Benard F (2018). Automated detection of sleep arousals from polysomnography data using a dense convolutional neural network. In: 2018 Computing in Cardiology Conference (CinC), vol. 45 . IEEE","DOI":"10.22489\/CinC.2018.232"},{"issue":"18","key":"1648_CR12","doi-asserted-by":"crossref","first-page":"5037","DOI":"10.3390\/s20185037","volume":"20","author":"H ElMoaqet","year":"2020","unstructured":"ElMoaqet H et al (2020) Deep recurrent neural networks for automatic detection of sleep apnea from single channel respiration signals. Sensors 20(18):5037","journal-title":"Sensors"},{"issue":"9","key":"1648_CR13","doi-asserted-by":"crossref","first-page":"16807","DOI":"10.3934\/mbe.2023749","volume":"20","author":"H Wang","year":"2023","unstructured":"Wang H et al (2023) Neural-seir: A flexible data-driven framework for precise prediction of epidemic disease. Math Biosci Eng 20(9):16807\u201316823","journal-title":"Math Biosci Eng"},{"key":"1648_CR14","unstructured":"Bear D, et al (2020). Learning physical graph representations from visual scenes. In: Advances in Neural Information Processing Systems 33, 6027\u20136039"},{"key":"1648_CR15","doi-asserted-by":"crossref","unstructured":"Xiang H, Zeng T, Yang Y (2020). A novel sleep stage classification via combination of fast representation learning and semantic-to-signal learning. In: 2020 International Joint Conference on Neural Networks (IJCNN) . IEEE","DOI":"10.1109\/IJCNN48605.2020.9206994"},{"issue":"70","key":"1648_CR16","first-page":"1","volume":"21","author":"SM Kazemi","year":"2020","unstructured":"Kazemi SM et al (2020) Representation learning for dynamic graphs: A survey. J Mach Learn Res 21(70):1\u201373","journal-title":"J Mach Learn Res"},{"key":"1648_CR17","volume":"86","author":"R Li","year":"2023","unstructured":"Li R et al (2023) Convolutional neural network for screening of obstructive sleep apnea using snoring sounds. Biomed Signal Process Control 86:104966","journal-title":"Biomed Signal Process Control"},{"key":"1648_CR18","doi-asserted-by":"crossref","unstructured":"Ma G, et al (2015). Unsupervised snore detection from respiratory sound signals. In: 2015 IEEE International Conference on Digital Signal Processing (DSP) . IEEE","DOI":"10.1109\/ICDSP.2015.7251905"},{"key":"1648_CR19","unstructured":"Zhao Y, et al (2011). A snoring detector for osahs based on patient\u2019s individual personality. In: 2011 3rd International Conference on Awareness Science and Technology (iCAST) . IEEE"},{"key":"1648_CR20","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1007\/s11325-020-02227-6","volume":"25","author":"W Wang","year":"2021","unstructured":"Wang W et al (2021) Evaluating the performance of five scoring systems for prescreening obstructive sleep apnea-hypopnea syndrome. Sleep Breathing 25:1685\u20131692","journal-title":"Sleep Breathing"},{"issue":"3","key":"1648_CR21","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1080\/17476348.2021.1852081","volume":"15","author":"Y Liu","year":"2021","unstructured":"Liu Y et al (2021) Cognitive function and life quality of patients with moderate-to-severe obstructive sleep apnea-hypopnea syndrome in china. Expert Rev Resp Med 15(3):435\u2013440","journal-title":"Expert Rev Resp Med"},{"key":"1648_CR22","doi-asserted-by":"crossref","unstructured":"Chen L, Zou S, Wang J (2022). Association of obstructive sleep apnea syndrome (osa\/osahs) with coronary atherosclerosis risk: systematic review and meta-analysis. Computational and Mathematical Methods in Medicine","DOI":"10.1155\/2022\/8905736"},{"issue":"3","key":"1648_CR23","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.anl.2020.03.007","volume":"47","author":"H-Y Li","year":"2020","unstructured":"Li H-Y et al (2020) How to manage continuous positive airway pressure (cpap) failure-hybrid surgery and integrated treatment. Auris Nasus Larynx 47(3):335\u2013342","journal-title":"Auris Nasus Larynx"},{"key":"1648_CR24","volume":"9","author":"BKS Thong","year":"2022","unstructured":"Thong BKS et al (2022) Telehealth technology application in enhancing continuous positive airway pressure adherence in obstructive sleep apnea patients: A review of current evidence. Front Med 9:877765","journal-title":"Front Med"},{"issue":"14","key":"1648_CR25","doi-asserted-by":"crossref","first-page":"6622","DOI":"10.3390\/app11146622","volume":"11","author":"A Sheta","year":"2021","unstructured":"Sheta A et al (2021) Diagnosis of obstructive sleep apnea from ecg signals using machine learning and deep learning classifiers. Appl Sci 11(14):6622","journal-title":"Appl Sci"},{"issue":"5","key":"1648_CR26","doi-asserted-by":"crossref","DOI":"10.1016\/j.amjoto.2023.103964","volume":"44","author":"Y Song","year":"2023","unstructured":"Song Y et al (2023) Ahi estimation of osahs patients based on snoring classification and fusion model. Am J Otolaryngol 44(5):103964","journal-title":"Am J Otolaryngol"},{"key":"1648_CR27","volume":"102","author":"ESJ Jothi","year":"2022","unstructured":"Jothi ESJ, Anitha J, Hemanth DJ (2022) A photoplethysmography-based diagnostic support system for obstructive sleep apnea using deep learning approaches. Comp Electric Eng 102:108279","journal-title":"Comp Electric Eng"},{"issue":"5","key":"1648_CR28","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1109\/TBME.2022.3225268","volume":"70","author":"R Huttunen","year":"2022","unstructured":"Huttunen R et al (2022) A comparison of signal combinations for deep learning-based simultaneous sleep staging and respiratory event detection. IEEE Trans Biomed Eng 70(5):1704\u20131714","journal-title":"IEEE Trans Biomed Eng"},{"issue":"14","key":"1648_CR29","doi-asserted-by":"crossref","first-page":"6622","DOI":"10.3390\/app11146622","volume":"11","author":"A Sheta","year":"2021","unstructured":"Sheta A et al (2021) Diagnosis of obstructive sleep apnea from ecg signals using machine learning and deep learning classifiers. Appl Sci 11(14):6622","journal-title":"Appl Sci"},{"issue":"7","key":"1648_CR30","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1093\/sleep\/zsz306","volume":"43","author":"H Sun","year":"2020","unstructured":"Sun H et al (2020) Sleep staging from electrocardiography and respiration with deep learning. Sleep 43(7):306","journal-title":"Sleep"},{"key":"1648_CR31","doi-asserted-by":"crossref","unstructured":"Ramachandran A, Karuppiah A (2021). A survey on recent advances in machine learning based sleep apnea detection systems. Healthcare 9(7)","DOI":"10.3390\/healthcare9070914"},{"key":"1648_CR32","first-page":"1","volume":"71","author":"M Bahrami","year":"2022","unstructured":"Bahrami M, Forouzanfar M (2022) Sleep apnea detection from single-lead ecg: A comprehensive analysis of machine learning and deep learning algorithms. IEEE Trans Instrum Measure 71:1\u201311","journal-title":"IEEE Trans Instrum Measure"},{"key":"1648_CR33","doi-asserted-by":"crossref","unstructured":"Xing X, et al (2020). Multi-level attention graph neural network for clinically interpretable pathway-level biomarkers discovery. bioRxiv, 2020\u201312","DOI":"10.1101\/2020.12.03.409755"},{"key":"1648_CR34","doi-asserted-by":"crossref","unstructured":"Qiu X, et al (2024). Gk bertdta: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction. Computers in Biology and Medicine, 108376","DOI":"10.1016\/j.compbiomed.2024.108376"},{"issue":"8","key":"1648_CR35","doi-asserted-by":"crossref","first-page":"2980","DOI":"10.3390\/s22082980","volume":"22","author":"T Wierci\u0144ski","year":"2022","unstructured":"Wierci\u0144ski T et al (2022) Emotion recognition from physiological channels using graph neural network. Sensors 22(8):2980","journal-title":"Sensors"},{"key":"1648_CR36","doi-asserted-by":"crossref","unstructured":"Jia Z, et al (2020). Graphsleepnet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification. In: IJCAI, vol. 2021","DOI":"10.24963\/ijcai.2020\/184"},{"key":"1648_CR37","volume":"15","author":"Y Xu","year":"2021","unstructured":"Xu Y et al (2021) Classifying vulnerability to sleep deprivation using resting-state functional mri graph theory metrics. Front Neurosci 15:660365","journal-title":"Front Neurosci"},{"key":"1648_CR38","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2019.105116","volume":"184","author":"M Diykh","year":"2020","unstructured":"Diykh M, Li Y, Abdulla S (2020) Eeg sleep stages identification based on weighted undirected complex networks. Comp Methods Prog Biomed 184:105116","journal-title":"Comp Methods Prog Biomed"},{"key":"1648_CR39","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1109\/TNSRE.2022.3176004","volume":"30","author":"X Ji","year":"2022","unstructured":"Ji X, Li Y, Wen P (2022) Jumping knowledge based spatial-temporal graph convolutional networks for automatic sleep stage classification. IEEE Trans Neural Syst Rehab Eng 30:1464\u20131472","journal-title":"IEEE Trans Neural Syst Rehab Eng"},{"issue":"23","key":"1648_CR40","doi-asserted-by":"crossref","first-page":"9272","DOI":"10.3390\/s22239272","volume":"22","author":"Q Wang","year":"2022","unstructured":"Wang Q et al (2022) Multi-layer graph attention network for sleep stage classification based on eeg. Sensors 22(23):9272","journal-title":"Sensors"},{"issue":"2","key":"1648_CR41","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1109\/TCSII.2020.3014514","volume":"68","author":"Q Cai","year":"2020","unstructured":"Cai Q et al (2020) A graph-temporal fused dual-input convolutional neural network for detecting sleep stages from eeg signals. IEEE Trans Circuits Syst 68(2):777\u2013781","journal-title":"IEEE Trans Circuits Syst"},{"issue":"6","key":"1648_CR42","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1109\/JBHI.2014.2303991","volume":"18","author":"G Zhu","year":"2014","unstructured":"Zhu G, Li Y, Wen P (2014) Analysis and classification of sleep stages based on difference visibility graphs from a single-channel eeg signal. IEEE J Biomed Health Inform 18(6):1813\u20131821","journal-title":"IEEE J Biomed Health Inform"},{"key":"1648_CR43","volume":"244","author":"Y Hu","year":"2024","unstructured":"Hu Y, Shi W, Yeh C-H (2024) Spatiotemporal convolution sleep network based on graph attention mechanism with automatic feature extraction. Comput Methods Prog Biomed 244:107930","journal-title":"Comput Methods Prog Biomed"},{"key":"1648_CR44","doi-asserted-by":"crossref","unstructured":"Cisotto G, et al (2020). Comparison of attention-based deep learning models for eeg classification. arXiv preprint arXiv:2012.01074","DOI":"10.21203\/rs.3.rs-279263\/v1"},{"key":"1648_CR45","doi-asserted-by":"crossref","unstructured":"Chen T, et al (2022). Ms $$^{2} $$-gnn: Exploring gnn-based multimodal fusion network for depression detection. IEEE Transactions on Cybernetics","DOI":"10.1109\/TCYB.2022.3197127"},{"issue":"5","key":"1648_CR46","doi-asserted-by":"crossref","first-page":"622","DOI":"10.3390\/life12050622","volume":"12","author":"M Li","year":"2022","unstructured":"Li M, Chen H, Cheng Z (2022) An attention-guided spatiotemporal graph convolutional network for sleep stage classification. Life 12(5):622","journal-title":"Life"},{"issue":"9","key":"1648_CR47","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/10.867928","volume":"47","author":"B Kemp","year":"2000","unstructured":"Kemp B et al (2000) Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the eeg. IEEE Trans Biomed Eng 47(9):1185\u20131194","journal-title":"IEEE Trans Biomed Eng"},{"issue":"8","key":"1648_CR48","doi-asserted-by":"crossref","first-page":"0256111","DOI":"10.1371\/journal.pone.0256111","volume":"16","author":"D Alvarez-Estevez","year":"2021","unstructured":"Alvarez-Estevez D, Rijsman RM (2021) Inter-database validation of a deep learning approach for automatic sleep scoring. PloS one 16(8):0256111","journal-title":"PloS one"},{"issue":"1","key":"1648_CR49","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1038\/s41597-022-01545-6","volume":"9","author":"H Lee","year":"2022","unstructured":"Lee H et al (2022) A large collection of real-world pediatric sleep studies. Sci Data 9(1):421","journal-title":"Sci Data"},{"key":"1648_CR50","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"1648_CR51","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s42979-021-00782-7","volume":"2","author":"N Hasan","year":"2021","unstructured":"Hasan N et al (2021) Densenet convolutional neural networks application for predicting covid-19 using ct image. SN Comput Sci 2(5):389","journal-title":"SN Comput Sci"},{"issue":"11","key":"1648_CR52","doi-asserted-by":"crossref","first-page":"4152","DOI":"10.3390\/ijerph17114152","volume":"17","author":"T Zhu","year":"2020","unstructured":"Zhu T, Luo W, Yu F (2020) Convolution-and attention-based neural network for automated sleep stage classification. Int J Environm Res Public Health 17(11):4152","journal-title":"Int J Environm Res Public Health"},{"issue":"5","key":"1648_CR53","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/JBHI.2020.3022989","volume":"25","author":"N Strodthoff","year":"2020","unstructured":"Strodthoff N, Wagner P, Sch\u00e4fer J, Cimr L, Kreil D, Igel C, Tino P (2020) Deep learning for ecg analysis: Benchmarks and insights from ptb-xl. IEEE J Biomed Health Inform 25(5):1519\u20131528","journal-title":"IEEE J Biomed Health Inform"},{"key":"1648_CR54","doi-asserted-by":"crossref","unstructured":"Reddy L, Joshi G, Reddy M.G, Prabhu S.S (2021). Imle-net: An interpretable multi-level multi-channel model for ecg classification. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE","DOI":"10.1109\/SMC52423.2021.9658706"},{"issue":"1","key":"1648_CR55","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41746-020-0291-x","volume":"3","author":"N Sridhar","year":"2020","unstructured":"Sridhar N, Rai A, Jung T-P, Cvetkovic B (2020) Deep learning for automated sleep staging using instantaneous heart rate. NPJ Digital Med 3(1):106","journal-title":"NPJ Digital Med"},{"key":"1648_CR56","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TNSRE.2021.3076234","volume":"29","author":"E Eldele","year":"2021","unstructured":"Eldele E, Shaban H, Khalil D, Elsaid MA (2021) An attention-based deep learning approach for sleep stage classification with single-channel eeg. IEEE Trans Neural Syst Rehab Eng 29:809\u2013818","journal-title":"IEEE Trans Neural Syst Rehab Eng"},{"key":"1648_CR57","doi-asserted-by":"crossref","unstructured":"Hu R, Singh A (2021). Unit: Multimodal multitask learning with a unified transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision","DOI":"10.1109\/ICCV48922.2021.00147"},{"issue":"9","key":"1648_CR58","first-page":"5903","volume":"44","author":"H Phan","year":"2021","unstructured":"Phan H, Khalighi S, Ghassemi M, Deters R (2021) Xsleepnet: Multi-view sequential model for automatic sleep staging. IEEE Trans Pattern Anal Mach Intell 44(9):5903\u20135915","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1648_CR59","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1109\/TNSRE.2022.3220372","volume":"31","author":"W Zhou","year":"2022","unstructured":"Zhou W, Huang Y, Zhou Z, Liu Z, Tang L, Yu X (2022) A lightweight segmented attention network for sleep staging by fusing local characteristics and adjacent information. IEEE Trans Neural Syst Rehab Eng 31:238\u2013247","journal-title":"IEEE Trans Neural Syst Rehab Eng"},{"key":"1648_CR60","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104351","volume":"80","author":"L Ding","year":"2023","unstructured":"Ding L et al (2023) Automatically detecting apnea-hypopnea snoring signal based on vgg19+ lstm. Biomed Signal Process Control 80:104351","journal-title":"Biomed Signal Process Control"},{"issue":"5","key":"1648_CR61","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1109\/JBHI.2023.3253728","volume":"27","author":"H Zhu","year":"2023","unstructured":"Zhu H, Dong G, Huang G, Qian Y (2023) Masksleepnet: A cross-modality adaptation neural network for heterogeneous signals processing in sleep staging. IEEE J Biomed Health Inform 27(5):2353\u20132364","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"1648_CR62","doi-asserted-by":"crossref","first-page":"17730","DOI":"10.1038\/s41598-023-45020-7","volume":"13","author":"S Morokuma","year":"2023","unstructured":"Morokuma S, Tsunoda H, Nagata M, Yagyu K, Takahashi K (2023) Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders. Sci Rep 13(1):17730","journal-title":"Sci Rep"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01648-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01648-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01648-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:18:06Z","timestamp":1738268286000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01648-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,18]]},"references-count":62,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1648"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01648-0","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,18]]},"assertion":[{"value":"20 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Ethics approval was obtained from the Ethics Committee of the EENT Hospital of Fudan University (No.2022140) and the study was registered in the Chinese Clinical Trial Registry (ChiCTR2300069223). Informed consent was obtained from all patients before the procedure.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"44"}}