{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T20:06:10Z","timestamp":1760385970496,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031346217"},{"type":"electronic","value":"9783031346224"}],"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\/978-3-031-34622-4_2","type":"book-chapter","created":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T20:25:17Z","timestamp":1686428717000},"page":"17-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Epileptic Seizure Prediction Using Bandpass Filtering and\u00a0Convolutional Neural Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6557-916X","authenticated-orcid":false,"given":"Nabiha","family":"Mustaqeem","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7932-1541","authenticated-orcid":false,"given":"Tasnia","family":"Rahman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0341-7576","authenticated-orcid":false,"given":"Jannatul Ferdous Binta Kalam","family":"Priyo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2138-8334","authenticated-orcid":false,"given":"Mohammad Zavid","family":"Parvez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9468-5033","authenticated-orcid":false,"given":"Tanvir","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,11]]},"reference":[{"key":"2_CR1","unstructured":"Butterworth filter design, equations and calculations. https:\/\/www.elprocus.com\/butterworth-filter-formula-and-calculations\/"},{"key":"2_CR2","unstructured":"Steven, C., Obsorne, P., Joseph, I.: Who Gets Epilepsy? Epilepsy Foundation. Kernel description (2013). https:\/\/www.epilepsy.com\/learn\/about-epilepsy-basics\/who-getsepilepsy"},{"key":"2_CR3","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.jneumeth.2015.06.010","volume":"260","author":"K Gadhoumi","year":"2016","unstructured":"Gadhoumi, K., Lina, J.M., Mormann, F., Gotman, J.: Seizure prediction for therapeutic devices: a review. J. Neurosci. Methods 260, 270\u2013282 (2016). https:\/\/doi.org\/10.1016\/j.jneumeth.2015.06.010","journal-title":"J. Neurosci. Methods"},{"issue":"23","key":"2_CR4","doi-asserted-by":"publisher","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), E215-20 (2000)","journal-title":"Circulation"},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B.: Focal onset seizure prediction using convolutional networks. IEEE Trans. Biomed. Eng. (2017). https:\/\/doi.org\/10.1109\/TBME.2017.2785401","DOI":"10.1109\/TBME.2017.2785401"},{"key":"2_CR6","doi-asserted-by":"publisher","unstructured":"Kuhlmann, L., Lehnertz, K., Richardson, M., Schelter, B., Zaveri, H.: Seizure prediction - ready for a new era. Nat. Rev. Neurol. 14 (2018). https:\/\/doi.org\/10.1038\/s41582-018-0055-2","DOI":"10.1038\/s41582-018-0055-2"},{"key":"2_CR7","doi-asserted-by":"publisher","first-page":"170352","DOI":"10.1109\/ACCESS.2019.2955285","volume":"7","author":"CL Liu","year":"2019","unstructured":"Liu, C.L., Xiao, B., Hsaio, W.H., Tseng, V.S.: Epileptic seizure prediction with multi-view convolutional neural networks. IEEE Access 7, 170352\u2013170361 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2955285","journal-title":"IEEE Access"},{"issue":"2","key":"2_CR8","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1093\/BRAIN\/AWL241","volume":"130","author":"F Mormann","year":"2007","unstructured":"Mormann, F., Andrzejak, R.G., Elger, C.E., Lehnertz, K.: Seizure prediction: the long and winding road. Brain 130(2), 314\u2013333 (2007). https:\/\/doi.org\/10.1093\/BRAIN\/AWL241","journal-title":"Brain"},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"39998","DOI":"10.1109\/ACCESS.2020.2976866","volume":"8","author":"S Muhammad Usman","year":"2020","unstructured":"Muhammad Usman, S., Khalid, S., Aslam, M.H.: Epileptic seizures prediction using deep learning techniques. IEEE Access 8, 39998\u201340007 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2976866","journal-title":"IEEE Access"},{"key":"2_CR10","unstructured":"Nall, R.: What you should know about seizures (2021). https:\/\/www.healthline.com\/health\/seizures"},{"issue":"11","key":"2_CR11","doi-asserted-by":"publisher","first-page":"2284","DOI":"10.1109\/TNSRE.2019.2943707","volume":"27","author":"AR Ozcan","year":"2019","unstructured":"Ozcan, A.R., Erturk, S.: Seizure prediction in scalp EEG using 3D convolutional neural networks with an image-based approach. IEEE Trans. Neural Syst. Rehabil. Eng. 27(11), 2284\u20132293 (2019). https:\/\/doi.org\/10.1109\/TNSRE.2019.2943707","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"1","key":"2_CR12","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1109\/TBME.2016.2553131","volume":"64","author":"MZ Parvez","year":"2017","unstructured":"Parvez, M.Z., Paul, M.: Seizure prediction using undulated global and local features. IEEE Trans. Biomed. Eng. 64(1), 208\u2013217 (2017). https:\/\/doi.org\/10.1109\/TBME.2016.2553131","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"Qin, Y., Zheng, H., Chen, W., Qin, Q., Han, C., Che, Y.: Patient-specific seizure prediction with scalp EEG using convolutional neural network and extreme learning machine. In: 2020 39th Chinese Control Conference (CCC), pp. 7622\u20137625 (2020). https:\/\/doi.org\/10.23919\/CCC50068.2020.9189578","DOI":"10.23919\/CCC50068.2020.9189578"},{"key":"2_CR14","doi-asserted-by":"publisher","unstructured":"Stafstrom, C., Carmant, L.: Seizures and epilepsy: an overview for neuroscientists. Cold Spring Harbor Perspect. Med. 5, a022426\u2013a022426 (2015). https:\/\/doi.org\/10.1101\/cshperspect.a022426","DOI":"10.1101\/cshperspect.a022426"},{"key":"2_CR15","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.neunet.2018.04.018","volume":"105","author":"ND Truong","year":"2018","unstructured":"Truong, N.D., et al.: Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw. 105, 104\u2013111 (2018). https:\/\/doi.org\/10.1016\/j.neunet.2018.04.018","journal-title":"Neural Netw."},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Kavehei, O.: A generalised seizure prediction with convolutional neural networks for intracranial and scalp electroencephalogram data analysis (2017). https:\/\/doi.org\/10.48550\/arXiv.1707.01976","DOI":"10.48550\/arXiv.1707.01976"},{"key":"2_CR17","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.seizure.2019.08.006","volume":"71","author":"SM Usman","year":"2019","unstructured":"Usman, S.M., Khalid, S., Akhtar, R., Bortolotto, Z., Bashir, Z., Qiu, H.: Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies. Seizure 71, 258\u2013269 (2019). https:\/\/doi.org\/10.1016\/j.seizure.2019.08.006","journal-title":"Seizure"},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"Usman, S.M., Khalid, S., Bashir, Z.: Epileptic seizure prediction using scalp electroencephalogram signals. Biocybern. Biomed. Eng. 41(1), 211\u2013220 (2021). https:\/\/doi.org\/10.1016\/j.bbe.2021.01.001, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0208521621000024","DOI":"10.1016\/j.bbe.2021.01.001"},{"key":"2_CR19","unstructured":"Viglione, S., Walsh, G.: Proceedings: epileptic seizure prediction. Electroencephalogr. Clin. Neurophysiol. 39(4), 435\u2013436 (1975). http:\/\/europepmc.org\/abstract\/MED\/51767"},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Wang, Z., Yang, J., Sawan, M.: A novel multi-scale dilated 3D CNN for epileptic seizure prediction. In: 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), pp. 1\u20134 (2021). https:\/\/doi.org\/10.1109\/AICAS51828.2021.9458571","DOI":"10.1109\/AICAS51828.2021.9458571"},{"key":"2_CR21","unstructured":"Epilepsy and seizures: Provoked seizures, seizure disorder, and more. https:\/\/www.webmd.com\/epilepsy\/guide\/understanding-seizures-and-epilepsy\/"},{"key":"2_CR22","doi-asserted-by":"publisher","unstructured":"Yu, D., et al.: Deep convolutional neural networks with layer-wise context expansion and attention. In: Interspeech, pp. 17\u201321 (2016). https:\/\/doi.org\/10.21437\/Interspeech.2016-251","DOI":"10.21437\/Interspeech.2016-251"},{"issue":"2","key":"2_CR23","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1109\/JBHI.2019.2933046","volume":"24","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Guo, Y., Yang, P., Chen, W., Lo, B.: Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: review of available methodologies. IEEE J. Biomed. Health Inf. 24(2), 465\u2013474 (2019). https:\/\/doi.org\/10.1109\/JBHI.2019.2933046","journal-title":"IEEE J. Biomed. Health Inf."}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Machine Intelligence and Emerging Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34622-4_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,10]],"date-time":"2023-06-10T20:25:42Z","timestamp":1686428742000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34622-4_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031346217","9783031346224"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34622-4_2","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIET","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Intelligence and Emerging Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Noakhali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bangladesh","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 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miet2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/confmiet.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy plus","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"272","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":"104","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":"38% - 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","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":"2","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)"}}]}}