{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:19:33Z","timestamp":1742973573493,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819736256"},{"type":"electronic","value":"9789819736263"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-3626-3_22","type":"book-chapter","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T10:07:45Z","timestamp":1718878065000},"page":"297-311","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Depression Recognition Based on\u00a0Pre-trained ResNet-18 Model and\u00a0Brain Effective Connectivity Network"],"prefix":"10.1007","author":[{"given":"Xiaoying","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingwei","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailing","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"22_CR1","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.: Automated EEG-based screening of depression using deep convolutional neural network. Comput. Methods Programs Biomed. 161, 103\u2013113 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1007\/s11571-019-09553-w","volume":"13","author":"F Afshani","year":"2019","unstructured":"Afshani, F., Shalbaf, A., Shalbaf, R., Sleigh, J.: Frontal-temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia. Cogn. Neurodyn. 13, 531\u2013540 (2019)","journal-title":"Cogn. Neurodyn."},{"key":"22_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2021.108078","volume":"179","author":"H Akbari","year":"2021","unstructured":"Akbari, H., et al.: Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features. Appl. Acoust. 179, 108078 (2021)","journal-title":"Appl. Acoust."},{"key":"22_CR4","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, 1\u201312 (2019)","journal-title":"J. Med. Syst."},{"key":"22_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105570","volume":"146","author":"S Bagherzadeh","year":"2022","unstructured":"Bagherzadeh, S., Shahabi, M.S., Shalbaf, A.: Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Comput. Biol. Med. 146, 105570 (2022)","journal-title":"Comput. Biol. Med."},{"unstructured":"Cai, H., Gao, Y., Sun, S., Li, N., Hu, B.: Modma dataset: a multi-model open dataset for mental- disorder analysis (2020). http:\/\/modma.lzu.edu.cn\/data\/index\/","key":"22_CR6"},{"key":"22_CR7","first-page":"1","volume":"2018","author":"H Cai","year":"2018","unstructured":"Cai, H., et al.: A pervasive approach to EEG-based depression detection. Complexity 2018, 1\u201313 (2018)","journal-title":"Complexity"},{"key":"22_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2021.837149","volume":"12","author":"H Chang","year":"2022","unstructured":"Chang, H., Zong, Y., Zheng, W., Tang, C., Zhu, J., Li, X.: Depression assessment method: an EEG emotion recognition framework based on spatiotemporal neural network. Front. Psych. 12, 837149 (2022)","journal-title":"Front. Psych."},{"issue":"8","key":"22_CR9","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.3173","volume":"13","author":"J Chang","year":"2023","unstructured":"Chang, J., Choi, Y.: Depression diagnosis based on electroencephalography power ratios. Brain Behav. 13(8), e3173 (2023)","journal-title":"Brain Behav."},{"key":"22_CR10","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.jad.2022.09.010","volume":"318","author":"R Cos\u00edo-Guirado","year":"2022","unstructured":"Cos\u00edo-Guirado, R., et al.: Diagnosis of late-life depression using structural equation modeling and dynamic effective connectivity during resting fMRI. J. Affect. Disord. 318, 246\u2013254 (2022)","journal-title":"J. Affect. Disord."},{"doi-asserted-by":"crossref","unstructured":"Do, L.: American psychiatric association diagnostic and statistical manual of mental disorders (DSM-IV). In: Encyclopedia of Child Behavior and Development, pp. 84\u201385 (2011)","key":"22_CR11","DOI":"10.1007\/978-0-387-79061-9_113"},{"key":"22_CR12","doi-asserted-by":"publisher","first-page":"112850","DOI":"10.1109\/ACCESS.2021.3103047","volume":"9","author":"C Greco","year":"2021","unstructured":"Greco, C., Matarazzo, O., Cordasco, G., Vinciarelli, A., Callejas, Z., Esposito, A.: Discriminative power of EEG-based biomarkers in major depressive disorder: A systematic review. IEEE Access 9, 112850\u2013112870 (2021)","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Group, B.D.W., et\u00a0al.: Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Therapeut. 69(3), 89\u201395 (2001)","key":"22_CR13","DOI":"10.1067\/mcp.2001.113989"},{"unstructured":"Hasanzadeh, F., Mohebbi, M., Rostami, R.: Effect of functional connectivity measures on characteristics of EEG based brain networks in MDD patients. In: 6th Basic and Clinical Neuroscience Congress (2017)","key":"22_CR14"},{"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)","key":"22_CR15","DOI":"10.1109\/CVPR.2016.90"},{"key":"22_CR16","doi-asserted-by":"publisher","first-page":"768","DOI":"10.3389\/fpsyt.2018.00768","volume":"9","author":"N Jaworska","year":"2019","unstructured":"Jaworska, N., De la Salle, S., Ibrahim, M.H., Blier, P., Knott, V.: Leveraging machine learning approaches for predicting antidepressant treatment response using electroencephalography (EEG) and clinical data. Front. Psych. 9, 768 (2019)","journal-title":"Front. Psych."},{"issue":"6","key":"22_CR17","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1001\/jamapsychiatry.2015.0071","volume":"72","author":"RH Kaiser","year":"2015","unstructured":"Kaiser, R.H., Andrews-Hanna, J.R., Wager, T.D., Pizzagalli, D.A.: Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiat. 72(6), 603\u2013611 (2015)","journal-title":"JAMA Psychiat."},{"key":"22_CR18","doi-asserted-by":"publisher","first-page":"8835","DOI":"10.1109\/ACCESS.2021.3049427","volume":"9","author":"DM Khan","year":"2021","unstructured":"Khan, D.M., Yahya, N., Kamel, N., Faye, I.: Automated diagnosis of major depressive disorder using brain effective connectivity and 3d convolutional neural network. IEEE Access 9, 8835\u20138846 (2021)","journal-title":"IEEE Access"},{"key":"22_CR19","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.3389\/fphys.2018.01385","volume":"9","author":"K Lebiecka","year":"2018","unstructured":"Lebiecka, K., Zuchowicz, U., Wozniak-Kwasniewska, A., Szekely, D., Olejarczyk, E., David, O.: Complexity analysis of EEG data in persons with depression subjected to transcranial magnetic stimulation. Front. Physiol. 9, 1385 (2018)","journal-title":"Front. Physiol."},{"issue":"1","key":"22_CR20","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.euroneuro.2012.10.011","volume":"23","author":"SJ Leistedt","year":"2013","unstructured":"Leistedt, S.J., Linkowski, P.: Brain, networks, depression, and more. Eur. Neuropsychopharmacol. 23(1), 55\u201362 (2013)","journal-title":"Eur. Neuropsychopharmacol."},{"issue":"11","key":"22_CR21","doi-asserted-by":"publisher","first-page":"2078","DOI":"10.1016\/j.clinph.2014.12.026","volume":"126","author":"Y Li","year":"2015","unstructured":"Li, Y., Cao, D., Wei, L., Tang, Y., Wang, J.: Abnormal functional connectivity of EEG gamma band in patients with depression during emotional face processing. Clin. Neurophysiol. 126(11), 2078\u20132089 (2015)","journal-title":"Clin. Neurophysiol."},{"issue":"6","key":"22_CR22","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.3390\/s17061385","volume":"17","author":"SC Liao","year":"2017","unstructured":"Liao, S.C., Wu, C.T., Huang, H.C., Cheng, W.T., Liu, Y.H.: Major depression detection from EEG signals using kernel eigen-filter-bank common spatial patterns. Sensors 17(6), 1385 (2017)","journal-title":"Sensors"},{"issue":"5","key":"22_CR23","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1016\/j.neubiorev.2010.12.012","volume":"35","author":"X Liu","year":"2011","unstructured":"Liu, X., Hairston, J., Schrier, M., Fan, J.: Common and distinct networks underlying reward valence and processing stages: a meta-analysis of functional neuroimaging studies. Neurosci. Biobehav. Rev. 35(5), 1219\u20131236 (2011)","journal-title":"Neurosci. Biobehav. Rev."},{"key":"22_CR24","doi-asserted-by":"publisher","first-page":"92630","DOI":"10.1109\/ACCESS.2019.2927121","volume":"7","author":"H Peng","year":"2019","unstructured":"Peng, H., et al.: Multivariate pattern analysis of EEG-based functional connectivity: a study on the identification of depression. IEEE Access 7, 92630\u201392641 (2019)","journal-title":"IEEE Access"},{"key":"22_CR25","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s11571-020-09619-0","volume":"15","author":"A Saeedi","year":"2021","unstructured":"Saeedi, A., Saeedi, M., Maghsoudi, A., Shalbaf, A.: Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach. Cogn. Neurodyn. 15, 239\u2013252 (2021)","journal-title":"Cogn. Neurodyn."},{"doi-asserted-by":"crossref","unstructured":"Sanchez, M.M., et al.: A machine learning algorithm to discriminating between bipolar and major depressive disorders based on resting EEG data. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2635\u20132638. IEEE (2022)","key":"22_CR26","DOI":"10.1109\/EMBC48229.2022.9871453"},{"issue":"3","key":"22_CR27","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1016\/j.bbe.2021.06.006","volume":"41","author":"MS Shahabi","year":"2021","unstructured":"Shahabi, M.S., Shalbaf, A., Maghsoudi, A.: Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on EEG. Biocybern. Biomed. Eng. 41(3), 946\u2013959 (2021)","journal-title":"Biocybern. Biomed. Eng."},{"issue":"20","key":"22_CR28","first-page":"22","volume":"59","author":"DV Sheehan","year":"1998","unstructured":"Sheehan, D.V., et al.: The mini-international neuropsychiatric interview (MINI): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatry 59(20), 22\u201333 (1998)","journal-title":"J. Clin. Psychiatry"},{"doi-asserted-by":"crossref","unstructured":"Spitzer, R.: Validation and utility of a self-report version of prime-md: the PHQ primary care study. JAMA 282 (1999)","key":"22_CR29","DOI":"10.1001\/jama.282.18.1737"},{"unstructured":"Sun, S., Li, J., Chen, H., Gong, T., Li, X., Hu, B.: A study of resting-state EEG biomarkers for depression recognition. arXiv preprint arXiv:2002.11039 (2020)","key":"22_CR30"},{"issue":"3","key":"22_CR31","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1109\/TNSRE.2019.2894423","volume":"27","author":"S Sun","year":"2019","unstructured":"Sun, S., et al.: Graph theory analysis of functional connectivity in major depression disorder with high-density resting state EEG data. IEEE Trans. Neural Syst. Rehabil. Eng. 27(3), 429\u2013439 (2019)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"22_CR32","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/s11571-018-9496-y","volume":"12","author":"H Zeng","year":"2018","unstructured":"Zeng, H., Yang, C., Dai, G., Qin, F., Zhang, J., Kong, W.: EEG classification of driver mental states by deep learning. Cogn. Neurodyn. 12, 597\u2013606 (2018)","journal-title":"Cogn. Neurodyn."},{"doi-asserted-by":"crossref","unstructured":"Zhang, B., et\u00a0al.: Cross-subject seizure detection in EEGs using deep transfer learning. Comput. Math. Methods Med. 2020 (2020)","key":"22_CR33","DOI":"10.1155\/2020\/7902072"},{"issue":"4","key":"22_CR34","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1016\/j.clinph.2018.01.017","volume":"129","author":"M Zhang","year":"2018","unstructured":"Zhang, M., et al.: Randomized EEG functional brain networks in major depressive disorders with greater resilience and lower rich-club coefficient. Clin. Neurophysiol. 129(4), 743\u2013758 (2018)","journal-title":"Clin. Neurophysiol."}],"container-title":["Communications in Computer and Information Science","Digital Multimedia Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-3626-3_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T08:24:21Z","timestamp":1732263861000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-3626-3_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819736256","9789819736263"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-3626-3_22","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"21 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IFTC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Forum on Digital TV and Wireless Multimedia Communications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iftc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.siga.org.cn\/xshd\/iftc2023.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}