{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:19:06Z","timestamp":1767320346113,"version":"3.48.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032095121","type":"print"},{"value":"9783032095138","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-09513-8_18","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:14:07Z","timestamp":1767320047000},"page":"182-191","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TransGATNet: Hybrid Temporal-Frequency Features with\u00a0Graph-Attention Transformers for\u00a0Sleep Staging in\u00a0OSA Patients"],"prefix":"10.1007","author":[{"given":"Zhouxin","family":"Xue","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaicong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"American Academy of Sleep Medicine and others: Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. The report of an American Academy of sleep medicine task force. Sleep 22, 667\u2013689 (1999)","DOI":"10.1093\/sleep\/22.5.667"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Berry, R.B., et al.: AASM scoring manual updates for 2017 (version 2.4) (2017)","DOI":"10.5664\/jcsm.6576"},{"key":"18_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11886-017-0916-0","volume":"19","author":"FP Cappuccio","year":"2017","unstructured":"Cappuccio, F.P., Miller, M.A.: Sleep and cardio-metabolic disease. Curr. Cardiol. Rep. 19, 1\u20139 (2017)","journal-title":"Curr. Cardiol. Rep."},{"issue":"6","key":"18_CR4","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1097\/MCP.0b013e328312ed4a","volume":"14","author":"NA Collop","year":"2008","unstructured":"Collop, N.A.: Portable monitoring for the diagnosis of obstructive sleep apnea. Curr. Opin. Pulm. Med. 14(6), 525\u2013529 (2008)","journal-title":"Curr. Opin. Pulm. Med."},{"issue":"9","key":"18_CR5","doi-asserted-by":"publisher","first-page":"4204","DOI":"10.1109\/JBHI.2023.3284160","volume":"27","author":"Y Dai","year":"2023","unstructured":"Dai, Y., et al.: Multichannelsleepnet: a transformer-based model for automatic sleep stage classification with PSG. IEEE J. Biomed. Health Inform. 27(9), 4204\u20134215 (2023)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"18_CR6","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1111\/j.1365-2869.2008.00700.x","volume":"18","author":"H Danker-Hopfe","year":"2009","unstructured":"Danker-Hopfe, H., et al.: Interrater reliability for sleep scoring according to the rechtschaffen & kales and the new AASM standard. J. Sleep Res. 18(1), 74\u201384 (2009)","journal-title":"J. Sleep Res."},{"issue":"5","key":"18_CR7","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1053\/smrv.2002.0252","volume":"7","author":"L De Gennaro","year":"2003","unstructured":"De Gennaro, L., Ferrara, M.: Sleep spindles: an overview. Sleep Med. Rev. 7(5), 423\u2013440 (2003)","journal-title":"Sleep Med. Rev."},{"key":"18_CR8","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1109\/TNSRE.2021.3076234","volume":"29","author":"E Eldele","year":"2021","unstructured":"Eldele, E., et al.: An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 809\u2013818 (2021)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Jia, Z., et al.: Graphsleepnet: adaptive spatial-temporal graph convolutional networks for sleep stage classification. In: IJCAI, vol.\u00a02021, pp. 1324\u20131330 (2020)","DOI":"10.24963\/ijcai.2020\/184"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Jia, Z., et al.: Exploiting interactivity and heterogeneity for sleep stage classification via heterogeneous graph neural network. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/ICASSP49357.2023.10095397"},{"issue":"9","key":"18_CR11","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1109\/10.867928","volume":"47","author":"B Kemp","year":"2000","unstructured":"Kemp, B., Zwinderman, A.H., Tuk, B., Kamphuisen, H.A., Oberye, J.J.: Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans. Biomed. Eng. 47(9), 1185\u20131194 (2000)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"18_CR12","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1109\/TNSRE.2023.3238764","volume":"31","author":"G Kong","year":"2023","unstructured":"Kong, G., Li, C., Peng, H., Han, Z., Qiao, H.: EEG-based sleep stage classification via neural architecture search. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 1075\u20131085 (2023)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"18_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122551","volume":"240","author":"S Lee","year":"2024","unstructured":"Lee, S., Yu, Y., Back, S., Seo, H., Lee, K.: SleepyCO: Automatic sleep scoring with feature pyramid and contrastive learning. Expert Syst. Appl. 240, 122551 (2024)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"18_CR14","doi-asserted-by":"publisher","first-page":"573","DOI":"10.5665\/sleep.2548","volume":"36","author":"A Malhotra","year":"2013","unstructured":"Malhotra, A., et al.: Performance of an automated polysomnography scoring system versus computer-assisted manual scoring. Sleep 36(4), 573\u2013582 (2013)","journal-title":"Sleep"},{"issue":"5","key":"18_CR15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0216456","volume":"14","author":"S Mousavi","year":"2019","unstructured":"Mousavi, S., Afghah, F., Acharya, U.R.: Sleepeegnet: automated sleep stage scoring with sequence to sequence deep learning approach. PLoS ONE 14(5), e0216456 (2019)","journal-title":"PLoS ONE"},{"issue":"2","key":"18_CR16","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1053\/smrv.1999.0087","volume":"4","author":"T Penzel","year":"2000","unstructured":"Penzel, T., Conradt, R.: Computer based sleep recording and analysis. Sleep Med. Rev. 4(2), 131\u2013148 (2000)","journal-title":"Sleep Med. Rev."},{"issue":"1","key":"18_CR17","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1038\/s41746-021-00440-5","volume":"4","author":"M Perslev","year":"2021","unstructured":"Perslev, M., Darkner, S., Kempfner, L., Nikolic, M., Jennum, P.J., Igel, C.: U-sleep: resilient high-frequency sleep staging. NPJ Digit. Med. 4(1), 72 (2021)","journal-title":"NPJ Digit. Med."},{"issue":"9","key":"18_CR18","first-page":"5903","volume":"44","author":"H Phan","year":"2021","unstructured":"Phan, H., Ch\u00e9n, O.Y., Tran, M.C., Koch, P., Mertins, A., De Vos, M.: XsleepNet: multi-view sequential model for automatic sleep staging. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5903\u20135915 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"18_CR19","doi-asserted-by":"publisher","first-page":"2456","DOI":"10.1109\/TBME.2022.3147187","volume":"69","author":"H Phan","year":"2022","unstructured":"Phan, H., Mikkelsen, K., Ch\u00e9n, O.Y., Koch, P., Mertins, A., De Vos, M.: Sleeptransformer: automatic sleep staging with interpretability and uncertainty quantification. IEEE Trans. Biomed. Eng. 69(8), 2456\u20132467 (2022)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"18_CR20","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.smrv.2016.07.002","volume":"34","author":"CV Senaratna","year":"2017","unstructured":"Senaratna, C.V., et al.: Prevalence of obstructive sleep apnea in the general population: a systematic review. Sleep Med. Rev. 34, 70\u201381 (2017)","journal-title":"Sleep Med. Rev."},{"key":"18_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102037","volume":"61","author":"H Seo","year":"2020","unstructured":"Seo, H., Back, S., Lee, S., Park, D., Kim, T., Lee, K.: Intra-and inter-epoch temporal context network (IITnet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG. Biomed. Sig. Process. Control 61, 102037 (2020)","journal-title":"Biomed. Sig. Process. Control"},{"issue":"1","key":"18_CR22","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.jneumeth.2007.06.026","volume":"166","author":"R Srinivasan","year":"2007","unstructured":"Srinivasan, R., Winter, W.R., Ding, J., Nunez, P.L.: EEG and meg coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics. J. Neurosci. Methods 166(1), 41\u201352 (2007)","journal-title":"J. Neurosci. Methods"},{"issue":"11","key":"18_CR23","doi-asserted-by":"publisher","first-page":"1998","DOI":"10.1109\/TNSRE.2017.2721116","volume":"25","author":"A Supratak","year":"2017","unstructured":"Supratak, A., Dong, H., Wu, C., Guo, Y.: DeepSleepnet: a model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 25(11), 1998\u20132008 (2017)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"18_CR24","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.70092","volume":"10","author":"R Vallat","year":"2021","unstructured":"Vallat, R., Walker, M.P.: An open-source, high-performance tool for automated sleep staging. Elife 10, e70092 (2021)","journal-title":"Elife"},{"key":"18_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104429","volume":"81","author":"J Van Der Donckt","year":"2023","unstructured":"Van Der Donckt, J., et al.: Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring. Biomed. Sig. Process. Control 81, 104429 (2023)","journal-title":"Biomed. Sig. Process. Control"},{"issue":"8","key":"18_CR26","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1038\/nrn2868","volume":"11","author":"K Wulff","year":"2010","unstructured":"Wulff, K., Gatti, S., Wettstein, J.G., Foster, R.G.: Sleep and circadian rhythm disruption in psychiatric and neurodegenerative disease. Nat. Rev. Neurosci. 11(8), 589\u2013599 (2010)","journal-title":"Nat. Rev. Neurosci."},{"key":"18_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106806","volume":"220","author":"C Zhao","year":"2022","unstructured":"Zhao, C., Li, J., Guo, Y.: Sleepcontextnet: a temporal context network for automatic sleep staging based single-channel EEG. Comput. Methods Programs Biomed. 220, 106806 (2022)","journal-title":"Comput. Methods Programs Biomed."}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09513-8_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:14:09Z","timestamp":1767320049000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09513-8_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032095121","9783032095138"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09513-8_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2025\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}