{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:23:23Z","timestamp":1774448603041,"version":"3.50.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031062414","type":"print"},{"value":"9783031062421","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06242-1_8","type":"book-chapter","created":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T23:03:26Z","timestamp":1653347006000},"page":"74-83","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Sleep Apnea Diagnosis Using Complexity Features of\u00a0EEG Signals"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7224-3576","authenticated-orcid":false,"given":"Behnam","family":"Gholami","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1335-3617","authenticated-orcid":false,"given":"Mohammad Hossein","family":"Behboudi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4173-5268","authenticated-orcid":false,"given":"Ali","family":"Khadem","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0635-6799","authenticated-orcid":false,"given":"Afshin","family":"Shoeibi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7069-1714","authenticated-orcid":false,"given":"Juan M.","family":"Gorriz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Alqassim, S., et al.: Sleep apnea monitoring using mobile phones. In: International Conference on e-Health Networking, Applications and Services (Healthcom). IEEE (2012)","DOI":"10.1109\/HealthCom.2012.6379457"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Azim, Md.R., et al.: Analysis of EEG and EMG signals for detection of sleep disordered breathing events. In: International Conference on Electrical and Computer Engineering (2010)","DOI":"10.1109\/ICELCE.2010.5700776"},{"issue":"3","key":"8_CR3","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1109\/JBHI.2018.2845303","volume":"23","author":"A Bhattacharjee","year":"2018","unstructured":"Bhattacharjee, A., et al.: Sleep apnea detection based on Rician modeling of feature variation in multiband EEG signal. IEEE J. Biomed. Health Inform. 23(3), 1066\u201381074 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Bello, S.A., Alqasemi, U.: Computer Aided Detection of Obstructive Sleep Apnea from EEG Signals. SSRN 3890660 (2021)","DOI":"10.2139\/ssrn.3890660"},{"key":"8_CR5","unstructured":"Devuyst, S., Dutoit, T., Kerkhofs, M.: The DREAMS databases and assessment algorithm. Zenodo, Gen\u00e8ve (2005)"},{"issue":"4","key":"8_CR6","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1109\/JBHI.2017.2740120","volume":"22","author":"S Gutta","year":"2017","unstructured":"Gutta, S., et al.: Cardiorespiratory model-based data-driven approach for sleep apnea detection. IEEE J. Biomed. Health Inform. 22(4), 1036\u20131045 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"6","key":"8_CR7","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1007\/s11571-021-09684-z","volume":"15","author":"G Gaurav","year":"2021","unstructured":"Gaurav, G., Anand, R.S., Kumar, V.: EEG based cognitive task classification using multifractal detrended fluctuation analysis. Cogn. Neurodyn. 15(6), 999\u20131013 (2021)","journal-title":"Cogn. Neurodyn."},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Jayaraj, R., Mohan, J.: Classification of sleep apnea based on sub-band decomposition of EEG signals. Diagnostics 11(9) (2021)","DOI":"10.3390\/diagnostics11091571"},{"key":"8_CR9","volume-title":"Principles of Neural Science","author":"ER Kandel","year":"2000","unstructured":"Kandel, E.R., et al.: Principles of Neural Science, vol. 3. McGraw-Hill, New York (2000)"},{"key":"8_CR10","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.jocn.2018.11.027","volume":"62","author":"J Kang","year":"2019","unstructured":"Kang, J., et al.: EEG entropy analysis in autistic children. J. Clin. Neurosci. 62, 199\u2013206 (2019)","journal-title":"J. Clin. Neurosci."},{"key":"8_CR11","unstructured":"Karegar, F.P., Fallah, A., Rashidi, S.: ECG based human authentication with using Generalized Hurst Exponent. In: Iranian Conference on Electrical Engineering (ICEE) (2017)"},{"issue":"3","key":"8_CR12","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.cmpb.2005.06.012","volume":"80","author":"N Kannathal","year":"2005","unstructured":"Kannathal, N., et al.: Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed. 80(3), 187\u2013194 (2005)","journal-title":"Comput. Methods Programs Biomed."},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Lin, S.-Y., et al.: EEG signal analysis of patients with obstructive sleep apnea syndrome (OSAS) using power spectrum and fuzzy entropy. In: International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (2017)","DOI":"10.1109\/FSKD.2017.8393366"},{"issue":"1","key":"8_CR14","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.eplepsyres.2007.08.002","volume":"77","author":"X Li","year":"2007","unstructured":"Li, X., Ouyang, G., Richards, D.A.: Predictability analysis of absence seizures with permutation entropy. Epilepsy Res. 77(1), 70\u201374 (2007)","journal-title":"Epilepsy Res."},{"issue":"2","key":"8_CR15","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1109\/TNN.2007.908634","volume":"19","author":"D Liu","year":"2008","unstructured":"Liu, D., Pang, Z., Lloyd, S.R.: A neural network method for detection of obstructive sleep apnea and narcolepsy based on pupil size and EEG. IEEE Trans. Neural Netw. 19(2), 308\u2013318 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"8","key":"8_CR16","doi-asserted-by":"publisher","first-page":"1568","DOI":"10.3390\/app9081568","volume":"9","author":"Y Li","year":"2019","unstructured":"Li, Y., et al.: Interhemispheric brain switching correlates with severity of sleep-disordered breathing for obstructive sleep apnea patients. Appl. Sci. 9(8), 1568 (2019)","journal-title":"Appl. Sci."},{"key":"8_CR17","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.physa.2017.08.084","volume":"490","author":"S Lahmiri","year":"2018","unstructured":"Lahmiri, S.: Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients. Physica A Stat. Mech. Appl. 490, 378\u2013385 (2018)","journal-title":"Physica A Stat. Mech. Appl."},{"key":"8_CR18","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.neucom.2020.05.078","volume":"410","author":"JM Gorriz","year":"2020","unstructured":"Gorriz, J.M., et al.: Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications. Neurocomputing 410, 237\u2013270 (2020)","journal-title":"Neurocomputing"},{"issue":"2","key":"8_CR19","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s10916-008-9231-z","volume":"34","author":"DP Subha","year":"2010","unstructured":"Subha, D.P., et al.: EEG signal analysis: a survey. J. Med. Syst. 34(2), 195\u2013212 (2010)","journal-title":"J. Med. Syst."},{"issue":"1","key":"8_CR20","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1177\/1077546314528021","volume":"22","author":"A Sharma","year":"2016","unstructured":"Sharma, A., Amarnath, M., Kankar, P.K.: Feature extraction and fault severity classification in ball bearings. J. Vibr. Control 22(1), 176\u2013192 (2016)","journal-title":"J. Vibr. Control"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Saha, S., et al.: An approach for automatic sleep apnea detection based on entropy of multi-band EEG signal. In: IEEE Region 10 Conference (TENCON) (2016)","DOI":"10.1109\/TENCON.2016.7848033"},{"key":"8_CR22","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":"8_CR23","doi-asserted-by":"crossref","unstructured":"Shahnaz, C., Minhaz, A.T., Ahamed, S.T.: Sub-frame based apnea detection exploiting delta band power ratio extracted from EEG signals. In: IEEE Region 10 Conference (TENCON) (2016)","DOI":"10.1109\/TENCON.2016.7847987"},{"issue":"2","key":"8_CR24","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1007\/s11704-012-2872-6","volume":"6","author":"T Schluter","year":"2012","unstructured":"Schluter, T., Conrad, S.: An approach for automatic sleep stage scoring and apnea-hypopnea detection. Front. Comput. Sci. 6(2), 230\u2013241 (2012)","journal-title":"Front. Comput. Sci."},{"key":"8_CR25","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.bspc.2017.05.002","volume":"38","author":"MN Tibdewal","year":"2017","unstructured":"Tibdewal, M.N., et al.: Multiple entropies performance measure for detection and localization of multi-channel epileptic EEG. Biomed. Signal Process. Control 38, 158\u2013167 (2017)","journal-title":"Biomed. Signal Process. Control"},{"issue":"2","key":"8_CR26","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1109\/TIM.2019.2902809","volume":"69","author":"S Taran","year":"2019","unstructured":"Taran, S., Bajaj, V.: Sleep apnea detection using artificial bee colony optimize Hermite basis functions for EEG signals. IEEE Trans. Instrum. Meas. 69(2), 608\u2013616 (2019)","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Taran, S., et al.: Detection of sleep apnea events using electroencephalogram signals. Appl. Acoust. 181, 108\u2013137 (2021)","DOI":"10.1016\/j.apacoust.2021.108137"},{"key":"8_CR28","doi-asserted-by":"publisher","unstructured":"Uthayakumar, R.: Fractal dimension in Epileptic EEG signal analysis. In: Banerjee, S., Rondoni, L. (eds.) Applications of Chaos and Nonlinear Dynamics in Science and Engineering-Vol. 3, pp. 103\u2013157. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-34017-8_4","DOI":"10.1007\/978-3-642-34017-8_4"},{"issue":"6","key":"8_CR29","doi-asserted-by":"publisher","first-page":"4463","DOI":"10.1016\/j.eswa.2009.12.065","volume":"37","author":"ED Ubeyli","year":"2010","unstructured":"Ubeyli, E.D., et al.: Analysis of sleep EEG activity during hypopnoea episodes by least squares support vector machine employing AR coefficients. Expert Syst. Appl. 37(6), 4463\u20134467 (2010)","journal-title":"Expert Syst. Appl."},{"issue":"8","key":"8_CR30","doi-asserted-by":"publisher","first-page":"3313","DOI":"10.1007\/s12206-021-0705-y","volume":"35","author":"H Wang","year":"2021","unstructured":"Wang, H., Guo, Z., Du, W.: Diagnosis of rolling element bearing based on multifractal detrended fluctuation analyses and continuous hidden Markov model. J. Mech. Sci. Technol. 35(8), 3313\u20133322 (2021). https:\/\/doi.org\/10.1007\/s12206-021-0705-y","journal-title":"J. Mech. Sci. Technol."},{"key":"8_CR31","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1109\/ACCESS.2020.3038486","volume":"9","author":"Y Wang","year":"2020","unstructured":"Wang, Y., et al.: An efficient method to detect sleep hypopnea-apnea events based on EEG signals. IEEE Access 9, 641\u2013650 (2020)","journal-title":"IEEE Access"},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: A Robust Sleep Apnea-hypopnea Syndrome Automated Detection Method Based on Fuzzy Entropy Using Single Lead-EEG Signals (2021)","DOI":"10.21203\/rs.3.rs-558448\/v1"},{"key":"8_CR33","doi-asserted-by":"crossref","unstructured":"Xin, X., Yaru, Z., Sanli, Y., et al.: A New Method for Detecting Sleep Apnea. Research Square (2022)","DOI":"10.21203\/rs.3.rs-1116811\/v1"},{"key":"8_CR34","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/S0165-0114(99)80004-9","volume":"100","author":"LA Zadeh","year":"1999","unstructured":"Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 100, 9\u201334 (1999)","journal-title":"Fuzzy Sets Syst."},{"issue":"1","key":"8_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"X Zhao","year":"2021","unstructured":"Zhao, X., et al.: Classification of sleep apnea based on EEG sub-band signal characteristics. Sci. Rep. 11(1), 1\u201311 (2021)","journal-title":"Sci. Rep."},{"issue":"6","key":"8_CR36","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1007\/s10877-015-9664-0","volume":"29","author":"J Zhou","year":"2015","unstructured":"Zhou, J., Wu, X., Zeng, W.: Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine. J. Clin. Monit. Comput. 29(6), 767\u2013772 (2015). https:\/\/doi.org\/10.1007\/s10877-015-9664-0","journal-title":"J. Clin. Monit. Comput."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06242-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T23:03:53Z","timestamp":1653347033000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06242-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031062414","9783031062421"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06242-1_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 May 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWINAC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on the Interplay Between Natural and Artificial Computation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Puerto de la Cruz, Tenerife","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwinac2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwinac.org\/iwinac2022\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ConfMaster","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"203","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"121","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}