{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:54:18Z","timestamp":1757620458622,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":38,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819506972"},{"type":"electronic","value":"9789819506989"}],"license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"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\/978-981-95-0698-9_23","type":"book-chapter","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T07:28:09Z","timestamp":1753946889000},"page":"276-287","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spindle-UMamba: A Mamba-Based Attention-Unet Framework for\u00a0Effective Sleep Spindle Detection"],"prefix":"10.1007","author":[{"given":"Dong","family":"Xu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4934-1246","authenticated-orcid":false,"given":"Tao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Jiao","sequence":"additional","affiliation":[]},{"given":"Zhaoze","family":"Xian","sequence":"additional","affiliation":[]},{"given":"Xiaoli","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"23_CR1","doi-asserted-by":"publisher","first-page":"624","DOI":"10.3389\/fnhum.2015.00624","volume":"9","author":"M Adamczyk","year":"2015","unstructured":"Adamczyk, M., Genzel, L., Dresler, M., Steiger, A., Friess, E.: Automatic sleep spindle detection and genetic influence estimation using continuous wavelet transform. Front. Hum. Neurosci. 9, 624 (2015)","journal-title":"Front. Hum. Neurosci."},{"issue":"2","key":"23_CR2","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1109\/TBME.2011.2175225","volume":"59","author":"B Babadi","year":"2011","unstructured":"Babadi, B., McKinney, S.M., Tarokh, V., Ellenbogen, J.M.: DiBa: a data-driven Bayesian algorithm for sleep spindle detection. IEEE Trans. Biomed. Eng. 59(2), 483\u2013493 (2011)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"23_CR3","unstructured":"Berry, R.B., et al.: The AASM manual for the scoring of sleep and associated events. Rules Terminol. Tech. Specif. Darien Illinois Am. Acad. Sleep Med. 176(2012), 7 (2012)"},{"issue":"5","key":"23_CR4","doi-asserted-by":"publisher","first-page":"597","DOI":"10.5664\/jcsm.2172","volume":"8","author":"RB Berry","year":"2012","unstructured":"Berry, R.B., et al.: 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. J. Clin. Sleep Med. 8(5), 597\u2013619 (2012)","journal-title":"J. Clin. Sleep Med."},{"issue":"5","key":"23_CR5","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/0013-4694(80)90296-5","volume":"48","author":"K Campbell","year":"1980","unstructured":"Campbell, K., Kumar, A., Hofman, W.: Human and automatic validation of a phase-locked loop spindle detection system. Electroencephalogr. Clin. Neurophysiol. 48(5), 602\u2013605 (1980)","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"23_CR6","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.jneumeth.2019.03.017","volume":"321","author":"S Chambon","year":"2019","unstructured":"Chambon, S., Thorey, V., Arnal, P.J., Mignot, E., Gramfort, A.: DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal. J. Neurosci. Methods 321, 64\u201378 (2019)","journal-title":"J. Neurosci. Methods"},{"issue":"5","key":"23_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":"23_CR8","unstructured":"Gu, A., Dao, T.: Mamba: linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"23_CR10","first-page":"435","volume":"15","author":"A Hekmatmanesh","year":"2017","unstructured":"Hekmatmanesh, A., et al.: Sleep spindle detection and prediction using a mixture of time series and chaotic features. Adv. Electr. Electron. Eng. 15(3), 435 (2017)","journal-title":"Adv. Electr. Electron. Eng."},{"key":"23_CR11","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"issue":"3","key":"23_CR13","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.artmed.2007.04.003","volume":"40","author":"E Huupponen","year":"2007","unstructured":"Huupponen, E., G\u00f3mez-Herrero, G., Saastamoinen, A., V\u00e4rri, A., Hasan, J., Himanen, S.L.: Development and comparison of four sleep spindle detection methods. Artif. Intell. Med. 40(3), 157\u2013170 (2007)","journal-title":"Artif. Intell. Med."},{"issue":"2","key":"23_CR14","doi-asserted-by":"publisher","first-page":"026026","DOI":"10.1088\/1741-2552\/abd463","volume":"18","author":"D Jiang","year":"2021","unstructured":"Jiang, D., Ma, Y., Wang, Y.: A robust two-stage sleep spindle detection approach using single-channel EEG. J. Neural Eng. 18(2), 026026 (2021)","journal-title":"J. Neural Eng."},{"issue":"2","key":"23_CR15","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1109\/TNSRE.2020.2964597","volume":"28","author":"T Kinoshita","year":"2020","unstructured":"Kinoshita, T., et al.: Sleep spindle detection using rusboost and synchrosqueezed wavelet transform. IEEE Trans. Neural Syst. Rehabil. Eng. 28(2), 390\u2013398 (2020)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"3","key":"23_CR16","doi-asserted-by":"publisher","first-page":"036004","DOI":"10.1088\/1741-2552\/ab0933","volume":"16","author":"PM Kulkarni","year":"2019","unstructured":"Kulkarni, P.M., et al.: A deep learning approach for real-time detection of sleep spindles. J. Neural Eng. 16(3), 036004 (2019)","journal-title":"J. Neural Eng."},{"key":"23_CR17","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.jneumeth.2017.12.023","volume":"297","author":"D Lachner-Piza","year":"2018","unstructured":"Lachner-Piza, D., Epitashvili, N., Schulze-Bonhage, A., Stieglitz, T., Jacobs, J., D\u00fcmpelmann, M.: A single channel sleep-spindle detector based on multivariate classification of EEG epochs: MUSSDET. J. Neurosci. Methods 297, 31\u201343 (2018)","journal-title":"J. Neurosci. Methods"},{"key":"23_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.jneumeth.2018.08.014","volume":"316","author":"K Lacourse","year":"2019","unstructured":"Lacourse, K., Delfrate, J., Beaudry, J., Peppard, P., Warby, S.C.: A sleep spindle detection algorithm that emulates human expert spindle scoring. J. Neurosci. Methods 316, 3\u201311 (2019)","journal-title":"J. Neurosci. Methods"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Li, J., Yuan, Z., Wang, X.: MuFF-E: sleep spindle detection using multi-feature fusion and ensemble. In: 2023 International Conference on Sensing, Measurement & Data Analytics in the Era of Artificial Intelligence (ICSMD), pp.\u00a01\u20135 (2023)","DOI":"10.1109\/ICSMD60522.2023.10490534"},{"key":"23_CR20","first-page":"103031","volume":"37","author":"Y Liu","year":"2025","unstructured":"Liu, Y., et al.: VMamba: visual state space model. Adv. Neural. Inf. Process. Syst. 37, 103031\u2013103063 (2025)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"3","key":"23_CR21","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1177\/1073858413500854","volume":"20","author":"A L\u00fcthi","year":"2014","unstructured":"L\u00fcthi, A.: Sleep spindles: where they come from, what they do. Neuroscientist 20(3), 243\u2013256 (2014)","journal-title":"Neuroscientist"},{"issue":"24","key":"23_CR22","doi-asserted-by":"publisher","first-page":"10941","DOI":"10.1523\/JNEUROSCI.22-24-10941.2002","volume":"22","author":"M M\u00f6lle","year":"2002","unstructured":"M\u00f6lle, M., Marshall, L., Gais, S., Born, J.: Grouping of spindle activity during slow oscillations in human non-rapid eye movement sleep. J. Neurosci. 22(24), 10941\u201310947 (2002)","journal-title":"J. Neurosci."},{"issue":"2","key":"23_CR23","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.pediatrneurol.2006.09.014","volume":"36","author":"I Myatchin","year":"2007","unstructured":"Myatchin, I., Lagae, L.: Sleep spindle abnormalities in children with generalized spike-wave discharges. Pediatr. Neurol. 36(2), 106\u2013111 (2007)","journal-title":"Pediatr. Neurol."},{"key":"23_CR24","unstructured":"Oktay, O., et\u00a0al.: Attention U-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"issue":"6","key":"23_CR25","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1111\/jsr.12169","volume":"23","author":"C O\u2019reilly","year":"2014","unstructured":"O\u2019reilly, C., Gosselin, N., Carrier, J., Nielsen, T.: Montreal archive of sleep studies: an open-access resource for instrument benchmarking and exploratory research. J. Sleep Res. 23(6), 628\u2013635 (2014)","journal-title":"J. Sleep Res."},{"issue":"4","key":"23_CR26","doi-asserted-by":"publisher","first-page":"e12614","DOI":"10.1111\/jsr.12614","volume":"27","author":"CR Patti","year":"2018","unstructured":"Patti, C.R., Penzel, T., Cvetkovic, D.: Sleep spindle detection using multivariate gaussian mixture models. J. Sleep Res. 27(4), e12614 (2018)","journal-title":"J. Sleep Res."},{"issue":"5","key":"23_CR27","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.jpsychores.2004.02.001","volume":"56","author":"D Petit","year":"2004","unstructured":"Petit, D., Gagnon, J.F., Fantini, M.L., Ferini-Strambi, L., Montplaisir, J.: Sleep and quantitative EEG in neurodegenerative disorders. J. Psychosom. Res. 56(5), 487\u2013496 (2004)","journal-title":"J. Psychosom. Res."},{"key":"23_CR28","unstructured":"Qu, H., et al.: A survey of mamba. arXiv preprint arXiv:2408.01129 (2024)"},{"key":"23_CR29","doi-asserted-by":"crossref","unstructured":"Ray, L., et al.: Expert and crowd-sourced validation of an individualized sleep spindle detection method employing complex demodulation and individualized normalization. Front. Hum. Neurosci. 9 (2015)","DOI":"10.3389\/fnhum.2015.00507"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015, pp. 234\u2013241. Springer, Cham (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"43","key":"23_CR31","doi-asserted-by":"publisher","first-page":"14356","DOI":"10.1523\/JNEUROSCI.3028-10.2010","volume":"30","author":"J Tamminen","year":"2010","unstructured":"Tamminen, J., Payne, J.D., Stickgold, R., Wamsley, E.J., Gaskell, M.G.: Sleep spindle activity is associated with the integration of new memories and existing knowledge. J. Neurosci. 30(43), 14356\u201314360 (2010)","journal-title":"J. Neurosci."},{"key":"23_CR32","doi-asserted-by":"crossref","unstructured":"Tapia, N.I., Est\u00e9vez, P.A.: RED: deep recurrent neural networks for sleep EEG event detection. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207719"},{"issue":"4","key":"23_CR33","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1038\/nmeth.2855","volume":"11","author":"SC Warby","year":"2014","unstructured":"Warby, S.C., et al.: Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods. Nat. Methods 11(4), 385\u2013392 (2014)","journal-title":"Nat. Methods"},{"issue":"2","key":"23_CR34","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1001\/archpsyc.1969.01740140118016","volume":"20","author":"EA Wolpert","year":"1969","unstructured":"Wolpert, E.A.: A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Arch. Gen. Psychiatry 20(2), 246\u2013247 (1969)","journal-title":"Arch. Gen. Psychiatry"},{"key":"23_CR35","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"23_CR36","doi-asserted-by":"publisher","first-page":"1614","DOI":"10.1109\/TNSRE.2021.3105443","volume":"29","author":"J You","year":"2021","unstructured":"You, J., Jiang, D., Ma, Y., Wang, Y.: SpindleU-net: an adaptive U-net framework for sleep spindle detection in single-channel EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 1614\u20131623 (2021)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"23_CR37","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s00521-016-2445-y","volume":"29","author":"C Y\u00fccelba\u015f","year":"2018","unstructured":"Y\u00fccelba\u015f, C., Y\u00fccelba\u015f, \u015e, \u00d6z\u015fen, S., Tezel, G., K\u00fc\u00e7\u00e7\u00fckt\u00fcrk, S., Yosunkaya, \u015e: Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods. Neural Comput. Appl. 29, 17\u201333 (2018)","journal-title":"Neural Comput. Appl."},{"key":"23_CR38","doi-asserted-by":"crossref","unstructured":"Zhao, R., et al.: Sleep spindle detection based on non-experts: a validation study. PLoS ONE 12(5), e0177437 (2017)","DOI":"10.1371\/journal.pone.0177437"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0698-9_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T08:26:09Z","timestamp":1757319969000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0698-9_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"ISBN":["9789819506972","9789819506989"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0698-9_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,1]]},"assertion":[{"value":"1 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISBRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Bioinformatics Research and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Helsinki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Finland","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":"3 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isbra2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.helsinki.fi\/en\/conferences\/isbra2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}