{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T00:16:23Z","timestamp":1792800983703,"version":"4.5.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030198220","type":"print"},{"value":"9783030198237","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-19823-7_27","type":"book-chapter","created":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T00:24:22Z","timestamp":1557879862000},"page":"331-339","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Localization of Epileptic Foci by Using Convolutional Neural Network Based on iEEG"],"prefix":"10.1007","author":[{"given":"Linfeng","family":"Sui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuyang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qibin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshihisa","family":"Tanaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianting","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,5,12]]},"reference":[{"key":"27_CR1","unstructured":"World health organization (2019). Epilepsy. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/epilepsy"},{"issue":"4","key":"27_CR2","doi-asserted-by":"publisher","first-page":"046206","DOI":"10.1103\/PhysRevE.86.046206","volume":"86","author":"RG Andrzejak","year":"2012","unstructured":"Andrzejak, R.G., Schindler, K., Rummel, C.: Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E 86(4), 046206 (2012)","journal-title":"Phys. Rev. E"},{"issue":"11","key":"27_CR3","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.1002\/hbm.23730","volume":"38","author":"RT Schirrmeister","year":"2017","unstructured":"Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for eeg decoding and visualization. Hum. Brain Mapp. 38(11), 5391\u20135420 (2017)","journal-title":"Hum. Brain Mapp."},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Itakura, T., Tanaka, T.: Epileptic focus localization based on bivariate empirical mode decomposition and entropy. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1426\u20131429. IEEE (2017)","DOI":"10.1109\/APSIPA.2017.8282255"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Dash, D.P., Kolekar, M.H.: A discrete-wavelet-transform-and Hidden-Markov-Model-Based approach for epileptic focus localization. In: Biomedical Signal and Image Processing in Patient Care, pp. 34\u201345. IGI Global (2018)","DOI":"10.4018\/978-1-5225-2829-6.ch003"},{"issue":"7","key":"27_CR6","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.compbiomed.2004.05.001","volume":"35","author":"MK K\u0131ym\u0131k","year":"2005","unstructured":"K\u0131ym\u0131k, M.K., G\u00fcler, \u0130., Dizib\u00fcy\u00fck, A., Ak\u0131n, M.: Comparison of stft and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application. Comput. Biol. Med. 35(7), 603\u2013616 (2005)","journal-title":"Comput. Biol. Med."},{"issue":"4","key":"27_CR7","doi-asserted-by":"publisher","first-page":"385","DOI":"10.3390\/app7040385","volume":"7","author":"A Bhattacharyya","year":"2017","unstructured":"Bhattacharyya, A., Pachori, R.B., Upadhyay, A., Acharya, U.R.: Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl. Sci. 7(4), 385 (2017)","journal-title":"Appl. Sci."},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Roy, S., Kiral-Kornek, I., Harrer, S.: ChronoNet: a deep recurrent neural network for abnormal EEG identification. arXiv preprint arXiv:1802.00308 (2018)","DOI":"10.1007\/978-3-030-21642-9_8"},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.compbiomed.2017.09.017","volume":"100","author":"UR Acharya","year":"2018","unstructured":"Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270\u2013278 (2018)","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"27_CR10","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":"27_CR11","doi-asserted-by":"crossref","unstructured":"Wang, X., Huang, G., Zhou, Z., Gao, J.: Radar emitter recognition based on the short time Fourier transform and convolutional neural networks. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1\u20135. IEEE (2017)","DOI":"10.1109\/CISP-BMEI.2017.8302111"},{"key":"27_CR12","unstructured":"Acharya, U.R., et al.: Characterization of focal EEG signals: a review. Future Gener. Comput. Syst. (2018)"},{"issue":"2","key":"27_CR13","doi-asserted-by":"publisher","first-page":"669","DOI":"10.3390\/e17020669","volume":"17","author":"R Sharma","year":"2015","unstructured":"Sharma, R., Pachori, R.B., Acharya, U.R.: Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals. Entropy 17(2), 669\u2013691 (2015)","journal-title":"Entropy"},{"issue":"4","key":"27_CR14","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1002\/ima.22199","volume":"26","author":"S Deivasigamani","year":"2016","unstructured":"Deivasigamani, S., Senthilpari, C., Yong, W.H.: Classification of focal and nonfocal EEG signals using anfis classifier for epilepsy detection. Int. J. Imaging Syst. Technol. 26(4), 277\u2013283 (2016)","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"27_CR15","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.bspc.2016.05.004","volume":"29","author":"AB Das","year":"2016","unstructured":"Das, A.B., Bhuiyan, M.I.H.: Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed. Signal Process. Control 29, 11\u201321 (2016)","journal-title":"Biomed. Signal Process. Control"},{"key":"27_CR16","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.jocs.2017.03.022","volume":"20","author":"R Sharma","year":"2017","unstructured":"Sharma, R., Kumar, M., Pachori, R.B., Acharya, U.R.: Decision support system for focal EEG signals using tunable-Q wavelet transform. J. Comput. Sci. 20, 52\u201360 (2017)","journal-title":"J. Comput. Sci."},{"key":"27_CR17","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.patrec.2017.03.017","volume":"94","author":"V Gupta","year":"2017","unstructured":"Gupta, V., Priya, T., Yadav, A.K., Pachori, R.B., Acharya, U.R.: Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform. Pattern Recogn. Lett. 94, 180\u2013188 (2017)","journal-title":"Pattern Recogn. Lett."},{"issue":"10","key":"27_CR18","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s10916-017-0800-x","volume":"41","author":"N Sriraam","year":"2017","unstructured":"Sriraam, N., Raghu, S.: Classification of focal and non focal epileptic seizures using multi-features and svm classifier. J. Med. Syst. 41(10), 160 (2017)","journal-title":"J. Med. Syst."},{"key":"27_CR19","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.patrec.2017.05.007","volume":"94","author":"N Arunkumar","year":"2017","unstructured":"Arunkumar, N., et al.: Classification of focal and non focal EEG using entropies. Pattern Recogn. Lett. 94, 112\u2013117 (2017)","journal-title":"Pattern Recogn. Lett."}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-19823-7_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:33:39Z","timestamp":1709832819000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-19823-7_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030198220","9783030198237"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-19823-7_27","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"12 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hersonissos","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 May 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.aiai2019.eu\/","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":"Easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"101","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":"49","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":"6","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":"49% - 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":"2.3","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}