{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T07:42:28Z","timestamp":1769240548983,"version":"3.49.0"},"publisher-location":"Cham","reference-count":20,"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_7","type":"book-chapter","created":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T23:03:26Z","timestamp":1653347006000},"page":"63-73","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Automatic Diagnosis of\u00a0Schizophrenia in\u00a0EEG Signals Using Functional Connectivity Features and\u00a0CNN-LSTM Model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0635-6799","authenticated-orcid":false,"given":"Afshin","family":"Shoeibi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5826-3856","authenticated-orcid":false,"given":"Mitra","family":"Rezaei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6537-0438","authenticated-orcid":false,"given":"Navid","family":"Ghassemi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3676-7898","authenticated-orcid":false,"given":"Zahra","family":"Namadchian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4983-3698","authenticated-orcid":false,"given":"Assef","family":"Zare","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7069-1714","authenticated-orcid":false,"given":"Juan M.","family":"Gorriz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"issue":"1","key":"7_CR1","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1177\/155005941104200105","volume":"42","author":"M Ahmadlou","year":"2011","unstructured":"Ahmadlou, M., Adeli, H.: Fuzzy synchronization likelihood with application to attention-deficit\/hyperactivity disorder. Clin. EEG Neurosci. 42(1), 6\u201313 (2011)","journal-title":"Clin. EEG Neurosci."},{"issue":"4","key":"7_CR2","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1016\/j.physd.2011.09.008","volume":"241","author":"M Ahmadlou","year":"2012","unstructured":"Ahmadlou, M., Adeli, H.: Visibility graph similarity: a new measure of generalized synchronization in coupled dynamic systems. Physica D Nonlinear Phenomena 241(4), 326\u2013332 (2012)","journal-title":"Physica D Nonlinear Phenomena"},{"key":"7_CR3","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.neulet.2017.04.009","volume":"650","author":"M Ahmadlou","year":"2017","unstructured":"Ahmadlou, M., Adeli, H.: Complexity of weighted graph: a new technique to investigate structural complexity of brain activities with applications to aging and autism. Neurosci. Lett. 650, 103\u2013108 (2017)","journal-title":"Neurosci. Lett."},{"issue":"2","key":"7_CR4","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.jneumeth.2012.08.020","volume":"211","author":"M Ahmadlou","year":"2012","unstructured":"Ahmadlou, M., Adeli, H., Adeli, A.: Fuzzy synchronization likelihood-wavelet methodology for diagnosis of autism spectrum disorder. J. Neurosci. Methods 211(2), 203\u2013209 (2012)","journal-title":"J. Neurosci. Methods"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Cortes-Briones, J.A., Tapia-Rivas, N.I., D\u2019Souza, D.C., Estevez, P.A.: Going deep into schizophrenia with artificial intelligence. Schizophrenia Res. (2021)","DOI":"10.1016\/j.schres.2021.05.018"},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.2147\/NDT.S202418","volume":"15","author":"R de Filippis","year":"2019","unstructured":"de Filippis, R., et al.: Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review. Neuropsychiatr. Dis. Treat. 15, 1605 (2019)","journal-title":"Neuropsychiatr. Dis. Treat."},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.neucom.2020.05.078","volume":"410","author":"JM G\u00f3rriz","year":"2020","unstructured":"G\u00f3rriz, 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"},{"key":"7_CR8","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/j.neunet.2019.10.014","volume":"122","author":"P Lanillos","year":"2020","unstructured":"Lanillos, P., Oliva, D., Philippsen, A., Yamashita, Y., Nagai, Y., Cheng, G.: A review on neural network models of schizophrenia and autism spectrum disorder. Neural Netw. 122, 338\u2013363 (2020)","journal-title":"Neural Netw."},{"issue":"6","key":"7_CR9","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1109\/TFUZZ.2008.2005002","volume":"16","author":"F Liu","year":"2008","unstructured":"Liu, F., Mendel, J.M.: Encoding words into interval type-2 fuzzy sets using an interval approach. IEEE Trans. Fuzzy Syst. 16(6), 1503\u20131521 (2008)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Naira, T., Alberto, C.: Classification of people who suffer schizophrenia and healthy people by EEG signals using deep learning (2020)","DOI":"10.14569\/IJACSA.2019.0101067"},{"issue":"14","key":"7_CR11","doi-asserted-by":"publisher","first-page":"2870","DOI":"10.3390\/app9142870","volume":"9","author":"SL Oh","year":"2019","unstructured":"Oh, S.L., Vicnesh, J., Ciaccio, E.J., Yuvaraj, R., Acharya, U.R.: Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals. Appl. Sci. 9(14), 2870 (2019)","journal-title":"Appl. Sci."},{"issue":"11","key":"7_CR12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0188629","volume":"12","author":"E Olejarczyk","year":"2017","unstructured":"Olejarczyk, E., Jernajczyk, W.: Graph-based analysis of brain connectivity in schizophrenia. PLoS ONE 12(11), e0188629 (2017)","journal-title":"PLoS ONE"},{"key":"7_CR13","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Sadeghi, D., et al.: An overview on artificial intelligence techniques for diagnosis of schizophrenia based on magnetic resonance imaging modalities: methods, challenges, and future works. arXiv preprint arXiv:2103.03081 (2021)","DOI":"10.1016\/j.compbiomed.2022.105554"},{"issue":"4","key":"7_CR15","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1007\/s13246-020-00925-9","volume":"43","author":"A Shalbaf","year":"2020","unstructured":"Shalbaf, A., Bagherzadeh, S., Maghsoudi, A.: Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals. Phys. Eng. Sci. Med. 43(4), 1229\u20131239 (2020)","journal-title":"Phys. Eng. Sci. Med."},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Shoeibi, A., et al.: Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies. Biomed. Signal Process. Control 73, 103417 (2022)","DOI":"10.1016\/j.bspc.2021.103417"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Shoeibi, A., et al.: Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: a review. Comput. Biol. Med. 136, 104697 (2021)","DOI":"10.1016\/j.compbiomed.2021.104697"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Shoeibi, A., et al.: Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models. Front. Neuroinform. 15 (2021)","DOI":"10.3389\/fninf.2021.777977"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Singh, K., Singh, S., Malhotra, J.: Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 235(2), 167\u2013184 (2021)","DOI":"10.1177\/0954411920966937"},{"key":"7_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-1-4842-5558-2_1","volume-title":"Learn TensorFlow 2.0","author":"P Singh","year":"2020","unstructured":"Singh, P., Manure, A.: Introduction to TensorFlow 2.0. In: Learn TensorFlow 2.0, pp. 1\u201324. Apress, Berkeley, CA (2020). https:\/\/doi.org\/10.1007\/978-1-4842-5558-2_1"}],"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_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T23:03:57Z","timestamp":1653347037000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06242-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031062414","9783031062421"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06242-1_7","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)"}}]}}