{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:01:04Z","timestamp":1742954464164,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819906161"},{"type":"electronic","value":"9789819906178"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-0617-8_7","type":"book-chapter","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T14:04:57Z","timestamp":1677161097000},"page":"85-101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Coherence Matrix Based Early Infantile Epileptic Encephalopathy Analysis with\u00a0ResNet"],"prefix":"10.1007","author":[{"given":"Yaohui","family":"Chen","sequence":"first","affiliation":[]},{"given":"Xiaonan","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Runze","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Yuanmeng","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Tiejia","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Danping","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jiuwen","family":"Cao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"7_CR1","unstructured":"Epilepsy, A.: Proposal for revised classification of epilepsies and epileptic syndromes. In: The Treatment of Epilepsy: Principles & Practice, p. 354 (2006)"},{"issue":"6","key":"7_CR2","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1097\/00004691-200311000-00003","volume":"20","author":"S Ohtahara","year":"2003","unstructured":"Ohtahara, S., Yamatogi, Y.: Epileptic encephalopathies in early infancy with suppression-burst. J. Clin. Neurophysiol. 20(6), 398\u2013407 (2003)","journal-title":"J. Clin. Neurophysiol."},{"issue":"1","key":"7_CR3","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/S0387-7604(01)00392-8","volume":"24","author":"Y Yamatogi","year":"2002","unstructured":"Yamatogi, Y., Ohtahara, S.: Early-infantile epileptic encephalopathy with suppression-bursts, ohtahara syndrome; its overview referring to our 16 cases. Brain Develop. 24(1), 13\u201323 (2002)","journal-title":"Brain Develop."},{"issue":"6","key":"7_CR4","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.irbm.2019.08.004","volume":"40","author":"S Rukhsar","year":"2019","unstructured":"Rukhsar, S., Khan, Y.U., Farooq, O., Sarfraz, M., Khan, A.T.: Patient-specific epileptic seizure prediction in long-term scalp EEG signal using multivariate statistical process control. IRBM 40(6), 320\u2013331 (2019)","journal-title":"IRBM"},{"issue":"2","key":"7_CR5","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.clinph.2014.05.022","volume":"126","author":"M Bandarabadi","year":"2015","unstructured":"Bandarabadi, M., Teixeira, C.A., Rasekhi, J., Dourado, A.: Epileptic seizure prediction using relative spectral power features. Clin. Neurophysiol. 126(2), 237\u2013248 (2015)","journal-title":"Clin. Neurophysiol."},{"issue":"10","key":"7_CR6","doi-asserted-by":"publisher","first-page":"1906","DOI":"10.1016\/j.clinph.2012.03.001","volume":"123","author":"K Gadhoumi","year":"2012","unstructured":"Gadhoumi, K., Lina, J.-M., Gotman, J.: Discriminating preictal and interictal states in patients with temporal lobe epilepsy using wavelet analysis of intracerebral eeg. Clin. Neurophysiol. 123(10), 1906\u20131916 (2012)","journal-title":"Clin. Neurophysiol."},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput. Intell. Neurosci. (2007)","DOI":"10.1155\/2007\/80510"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: The use of time-frequency distributions for epileptic seizure detection in EEG recordings. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3\u20136. IEEE (2007)","DOI":"10.1109\/IEMBS.2007.4352208"},{"issue":"5","key":"7_CR9","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/TITB.2009.2017939","volume":"13","author":"AT Tzallas","year":"2009","unstructured":"Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Trans. Inf. Technol. Biomed. 13(5), 703\u2013710 (2009)","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"George, F., et al.: Epileptic seizure prediction using EEG images. In: 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 1595\u20131598. IEEE (2020)","DOI":"10.1109\/ICCSP48568.2020.9182327"},{"issue":"4","key":"7_CR11","doi-asserted-by":"publisher","first-page":"3914","DOI":"10.1007\/s11227-020-03426-4","volume":"77","author":"S Zhang","year":"2021","unstructured":"Zhang, S., Chen, D., Ranjan, R., Ke, H., Tang, Y., Zomaya, A.Y.: A lightweight solution to epileptic seizure prediction based on EEG synchronization measurement. J. Supercomput. 77(4), 3914\u20133932 (2021)","journal-title":"J. Supercomput."},{"key":"7_CR12","doi-asserted-by":"publisher","first-page":"1604","DOI":"10.1109\/TNSRE.2021.3103210","volume":"29","author":"X Yang","year":"2021","unstructured":"Yang, X., Zhao, J., Sun, Q., Jianbo, L., Ma, X.: An effective dual self-attention residual network for seizure prediction. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 1604\u20131613 (2021)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"4","key":"7_CR13","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.bspc.2011.07.007","volume":"7","author":"UR Acharya","year":"2012","unstructured":"Acharya, U.R., Molinari, F., Sree, S.V., Chattopadhyay, S., Ng, K.-H., Suri, J.S.: Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Process. Control 7(4), 401\u2013408 (2012)","journal-title":"Biomed. Signal Process. Control"},{"issue":"3","key":"7_CR14","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., Choo, M.L., Acharya, U.R., Sadasivan, P.K.: Entropies for detection of epilepsy in EEG. Comput. Methods Programs Biomed. 80(3), 187\u2013194 (2005)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"4","key":"7_CR15","doi-asserted-by":"publisher","first-page":"3284","DOI":"10.1016\/j.eswa.2009.09.051","volume":"37","author":"S Pravin Kumar","year":"2010","unstructured":"Pravin Kumar, S., Sriraam, N., Benakop, P.G., Jinaga, B.C.: Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Syst. Appl. 37(4), 3284\u20133291 (2010)","journal-title":"Expert Syst. Appl."},{"key":"7_CR16","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.neunet.2022.05.029","volume":"153","author":"R Zheng","year":"2022","unstructured":"Zheng, R., et al.: Scalp EEG functional connection and brain network in infants with west syndrome. Neural Netw. 153, 76\u201386 (2022)","journal-title":"Neural Netw."},{"key":"7_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102554","volume":"67","author":"J Cao","year":"2021","unstructured":"Cao, J., et al.: Using interictal seizure-free EEG data to recognise patients with epilepsy based on machine learning of brain functional connectivity. Biomed. Signal Process. Control 67, 102554 (2021)","journal-title":"Biomed. Signal Process. Control"},{"issue":"5","key":"7_CR18","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.biopsych.2018.11.011","volume":"85","author":"Z Sha","year":"2019","unstructured":"Sha, Z., Wager, T.D., Mechelli, A., He, Y.: Common dysfunction of large-scale neurocognitive networks across psychiatric disorders. Biol. Psychiatry 85(5), 379\u2013388 (2019)","journal-title":"Biol. Psychiatry"},{"issue":"2","key":"7_CR19","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.braindev.2014.04.004","volume":"37","author":"Y Toda","year":"2015","unstructured":"Toda, Y., et al.: High-frequency EEG activity in epileptic encephalopathy with suppression-burst. Brain Develop. 37(2), 230\u2013236 (2015)","journal-title":"Brain Develop."},{"key":"7_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108856","volume":"248","author":"Y Feng","year":"2022","unstructured":"Feng, Y., et al.: 3D residual-attention-deep-network-based childhood epilepsy syndrome classification. Knowl.-Based Syst. 248, 108856 (2022)","journal-title":"Knowl.-Based Syst."},{"issue":"4","key":"7_CR21","first-page":"1542","volume":"68","author":"H Dinghan","year":"2021","unstructured":"Dinghan, H., Cao, J., Lai, X., Wang, Y., Wang, S., Ding, Y.: Epileptic state classification by fusing hand-crafted and deep learning EEG features. IEEE Trans. Circuits Syst. II Express Briefs 68(4), 1542\u20131546 (2021)","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"issue":"12","key":"7_CR22","first-page":"3592","volume":"67","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Duanpo, W., Dong, F., Cao, J., Jiang, T., Liu, J.: A novel spike detection algorithm based on multi-channel of BECT EEG signals. IEEE Trans. Circuits Syst. II Express Briefs 67(12), 3592\u20133596 (2020)","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"issue":"2","key":"7_CR23","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1109\/TCDS.2020.3009020","volume":"13","author":"H Dinghan","year":"2021","unstructured":"Dinghan, H., Cao, J., Lai, X., Liu, J., Wang, S., Ding, Y.: Epileptic signal classification based on synthetic minority oversampling and blending algorithm. IEEE Trans. Cogn. Develop. Syst. 13(2), 368\u2013382 (2021)","journal-title":"IEEE Trans. Cogn. Develop. Syst."},{"key":"7_CR24","doi-asserted-by":"publisher","first-page":"1734","DOI":"10.1109\/TNSRE.2021.3107142","volume":"29","author":"X Zhendi","year":"2021","unstructured":"Zhendi, X., Wang, T., Cao, J., Bao, Z., Jiang, T., Gao, F.: BECT spike detection based on novel EEG sequence features and LSTM algorithms. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 1734\u20131743 (2021)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"2","key":"7_CR25","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1109\/TCDS.2021.3064228","volume":"14","author":"J Cao","year":"2022","unstructured":"Cao, J., Dinghan, H., Wang, Y., Wang, J., Lei, B.: Epileptic classification with deep-transfer-learning-based feature fusion algorithm. IEEE Trans. Cogn. Develop. Syst. 14(2), 684\u2013695 (2022)","journal-title":"IEEE Trans. Cogn. Develop. Syst."},{"issue":"4","key":"7_CR26","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1109\/TCDS.2019.2936441","volume":"12","author":"J Cao","year":"2020","unstructured":"Cao, J., Zhu, J., Wenbin, H., Kummert, A.: Epileptic signal classification with deep EEG features by stacked CNNs. IEEE Trans. Cogn. Develop. Syst. 12(4), 709\u2013722 (2020)","journal-title":"IEEE Trans. Cogn. Develop. Syst."},{"issue":"8","key":"7_CR27","doi-asserted-by":"publisher","first-page":"2895","DOI":"10.1109\/JBHI.2021.3057891","volume":"25","author":"J Cao","year":"2021","unstructured":"Cao, J., et al.: Unsupervised eye blink artifact detection from EEG with gaussian mixture model. IEEE J. Biomed. Health Inform. 25(8), 2895\u20132905 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"7_CR28","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"},{"key":"7_CR29","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Communications in Computer and Information Science","Cognitive Systems and Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-0617-8_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T12:09:35Z","timestamp":1684325375000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-0617-8_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819906161","9789819906178"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-0617-8_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"24 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This study has been approved by the Second Affiliated Hospital of Zhejiang University and registered in Chinese Clinical Trail Registry (ChiCTR1900020726). All patients gave their informed consent prior to their inclusion in the study.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Standards"}},{"value":"ICCSIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Cognitive Systems and Signal Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fuzhou","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsip.fzu.edu.cn\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"121","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":"47","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":"39% - 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":"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)"}}]}}