{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T19:00:39Z","timestamp":1775588439659,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789811982217","type":"print"},{"value":"9789811982224","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"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":[[2023]]},"DOI":"10.1007\/978-981-19-8222-4_6","type":"book-chapter","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T02:18:15Z","timestamp":1669688295000},"page":"60-72","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["SE-1DCNN-LSTM: A Deep Learning Framework for EEG-Based Automatic Diagnosis of Major Depressive Disorder and Bipolar Disorder"],"prefix":"10.1007","author":[{"given":"Ziyu","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Hui","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Dewen","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Kerang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,29]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1016\/j.jad.2015.10.042","volume":"190","author":"H He","year":"2016","unstructured":"He, H., Yu, Q., Du, Y., Victor, V., Victor, T.A., Drevets, W.C., et al.: Resting-state functional network connectivity in prefrontal regions differs between unmedicated patients with bipolar and major depressive disorders. J. Affect. Disord. 190, 483\u2013493 (2016). https:\/\/doi.org\/10.1016\/j.jad.2015.10.042","journal-title":"J. Affect. Disord."},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Hirschfeld, R., Cass A.R., Holt. D.C.L, Carlson.C.A.: Screening for bipolar disorder in patients treated for depression in a family medicine clinic. The J. American Board Family Medicine 18(4), 233\u2013239 (2005). https:\/\/doi.org\/10.3122\/jabfm.18.4.233","DOI":"10.3122\/jabfm.18.4.233"},{"issue":"6","key":"6_CR3","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1046\/j.1399-5618.2003.00074.x","volume":"5","author":"SN Ghaemi","year":"2015","unstructured":"Ghaemi, S.N., Hsu, D.J., SoldaniF, F., Goodwin, F.K.: Antidepressants in bipolar disorder: the case for caution. Bipolar Disord. 5(6), 421\u2013433 (2015). https:\/\/doi.org\/10.1046\/j.1399-5618.2003.00074.x","journal-title":"Bipolar Disord."},{"key":"6_CR4","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.cmpb.2018.04.012","volume":"161","author":"UR Acharya","year":"2018","unstructured":"Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H., Subha, D.P., et al.: Automated EEG-based screening of depression using deep convolutional neural network. Computer Methods Biomedicine Programs in Bio-medicine 161, 103\u2013113 (2018). https:\/\/doi.org\/10.1016\/j.cmpb.2018.04.012","journal-title":"Computer Methods Biomedicine Programs in Bio-medicine"},{"issue":"7","key":"6_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1345-y","volume":"43","author":"B Ay","year":"2019","unstructured":"Ay, B., et al.: Automated depression detection using deep representation and sequence learning with EEG signals. J. Med. Syst. 43(7), 1\u201312 (2019). https:\/\/doi.org\/10.1007\/s10916-019-1345-y","journal-title":"J. Med. Syst."},{"key":"6_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1007\/978-3-030-05587-5_31","volume-title":"Brain Informatics","author":"W Mao","year":"2018","unstructured":"Mao, W., Zhu, J., Li, X., Zhang, X., Sun, S.: Resting state EEG based depression recognition research using deep learning method. In: Wang, S., et al. (eds.) BI 2018. LNCS (LNAI), vol. 11309, pp. 329\u2013338. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-05587-5_31"},{"key":"6_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2019.103983","volume":"132","author":"W Mumtaz","year":"2019","unstructured":"Mumtaz, W., Qayyumb, A.: A deep learning framework for automatic diagnosis of unipolar depression. Int. J. Med. Informatics 132, 103983 (2019). https:\/\/doi.org\/10.1016\/j.ijmedinf.2019.103983","journal-title":"Int. J. Med. Informatics"},{"key":"6_CR8","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.compbiomed.2015.06.021","volume":"64","author":"T Erguzel","year":"2015","unstructured":"Erguzel, T., Cumhur, T., Merve, C.: A wrapper-based approach for feature selection and classification of major depressive disorder\u2013bipolar disorders. Comput. Biol. Med. 64, 127\u2013137 (2015). https:\/\/doi.org\/10.1016\/j.compbiomed.2015.06.021","journal-title":"Comput. Biol. Med."},{"issue":"6","key":"6_CR9","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1007\/s00521-015-1959-z","volume":"27","author":"TT Erguzel","year":"2015","unstructured":"Erguzel, T.T., Sayar, G.H., Tarhan, N.: Artificial intelligence approach to classify unipolar and bipolar depressive disorders. Neural Comput. Appl. 27(6), 1607\u20131616 (2015). https:\/\/doi.org\/10.1007\/s00521-015-1959-z","journal-title":"Neural Comput. Appl."},{"key":"6_CR10","doi-asserted-by":"publisher","unstructured":"Brooks, J.O., Wang, P.W, Ketter, T.A.: Functional brain imaging studies in bipolar disorder: focus on cerebral metabolism and blood flow. In: Yatham, L.N., Wang, P.W., Ketter, T.A.: (eds.) Bipolar Disorder. pp. 200\u2013209. Wiley Online Library (2010). https:\/\/doi.org\/10.1002\/9780470661277.ch15","DOI":"10.1002\/9780470661277.ch15"},{"key":"6_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2020.108885","volume":"346","author":"E Lashgari","year":"2020","unstructured":"Lashgari, E., Liang, D., Maoz, U.: Data augmentation for deep-learning-based electroencephalography. J. Neurosci. Methods 346, 108885 (2020). https:\/\/doi.org\/10.1016\/j.jneumeth.2020.108885","journal-title":"J. Neurosci. Methods"},{"key":"6_CR12","doi-asserted-by":"publisher","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.H.: Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Intelligence Machine 42(8), 2011\u20132023 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2019.2913372","DOI":"10.1109\/TPAMI.2019.2913372"},{"issue":"9","key":"6_CR13","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.1016\/j.neunet.2009.06.003","volume":"22","author":"S Fazli","year":"2009","unstructured":"Fazli, S., Popescu, F., Dan\u00f3czy, M.: Subject-independent mental state classification in single trials. Neural Netw. 22(9), 1305\u20131312 (2009). https:\/\/doi.org\/10.1016\/j.neunet.2009.06.003","journal-title":"Neural Netw."},{"issue":"3","key":"6_CR14","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/taffc.2018.2817622","volume":"11","author":"T Song","year":"2020","unstructured":"Song, T., Zheng, W., Song, P., Cui, Z.: EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans. Affect. Comput. 11(3), 532\u2013541 (2020). https:\/\/doi.org\/10.1109\/taffc.2018.2817622","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"5","key":"6_CR15","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2016","unstructured":"Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional network for EEG-based brain-computer interfaces. J. Neural Eng. 15(5), 056013 (2016). https:\/\/doi.org\/10.1088\/1741-2552\/aace8c","journal-title":"J. Neural Eng."},{"issue":"11","key":"6_CR16","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.1002\/hbm.23730","volume":"38","author":"RT Schirrmeiste","year":"2017","unstructured":"Schirrmeiste, R.T., Gemein, L., Eggensperger, K., Hutter, F., Ball, T.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38(11), 5391\u20135420 (2017). https:\/\/doi.org\/10.1002\/hbm.23730","journal-title":"Hum. Brain Mapp."},{"issue":"6","key":"6_CR17","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K.M., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Analysis Intelligence Machine 39(6), 1137\u20131149 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans. Pattern Analysis Intelligence Machine"},{"key":"6_CR18","doi-asserted-by":"publisher","first-page":"4115","DOI":"10.1073\/pnas.062381899","volume":"99","author":"JR Gray","year":"2002","unstructured":"Gray, J.R., Braver, T.S., Raichle, M.E.: Integration of emotion and cognition in the lateral prefrontal cortex. Proc. Natl. Acad. Sci. U.S.A. 99, 4115\u20134120 (2002). https:\/\/doi.org\/10.1073\/pnas.062381899","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"6_CR19","doi-asserted-by":"publisher","unstructured":"Hosokawa, T., Momose, T., Kasai, K.: Brain glucose metabolism difference between bipolar and unipolar mood disorders in depressed and euthymic states. Progress in Neuro-Psychopharmacology and Biological Psychiatry 33(2), 243\u2013250 (2009). https:\/\/doi.org\/10.1016\/j.pnpbp.2008.11.014","DOI":"10.1016\/j.pnpbp.2008.11.014"},{"key":"6_CR20","unstructured":"Kopecek, M., Barbora, T., Peter, S., Martin, B., Martin, B.: QEEG changes during switch rom depression to hypomania\/mania: A case report. Neuro endocrinology letters. 29(3), 295\u2013302 (2008)"},{"issue":"18","key":"6_CR21","doi-asserted-by":"publisher","first-page":"1693","DOI":"10.3969\/j.issn.1673-5374.2013.18.007","volume":"8","author":"J Li","year":"2013","unstructured":"Li, J., Xu, C., Cao, X., Gao, Q., Wang, Y., Wang, Y.F., et al.: Abnormal activation of the occipital lobes during emotion picture processing in major depressive disorder patients. Neural Regen. Res. 8(18), 1693\u20131701 (2013). https:\/\/doi.org\/10.3969\/j.issn.1673-5374.2013.18.007","journal-title":"Neural Regen. Res."}],"container-title":["Communications in Computer and Information Science","Human Brain and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-8222-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T02:19:53Z","timestamp":1669688393000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-8222-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,29]]},"ISBN":["9789811982217","9789811982224"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-8222-4_6","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,29]]},"assertion":[{"value":"29 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HBAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Human Brain and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vienna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","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":"23 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ijcaihbai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/hbai2022.github.io\/","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":"Easy chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21","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":"19","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":"90% - 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":"3","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":"6","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)"}}]}}