{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T07:03:15Z","timestamp":1775977395109,"version":"3.50.1"},"reference-count":30,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","epilepsybehavior.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Epilepsy & Behavior"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1016\/j.yebeh.2024.109744","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T22:43:41Z","timestamp":1710974621000},"page":"109744","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["eDeeplepsy: An artificial neural framework to reveal different brain states in children with epileptic spasms"],"prefix":"10.1016","volume":"154","author":[{"given":"Alberto","family":"Nogales","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c1lvaro J.","family":"Garc\u00eda-Tejedor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Serrano Vara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arturo","family":"Ugalde-Canitrot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"7788","key":"10.1016\/j.yebeh.2024.109744_b0005","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","article-title":"International evaluation of an AI system for breast cancer screening","volume":"577","author":"McKinney","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.yebeh.2024.109744_b0010","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1111\/epi.16333","article-title":"Machine learning applications in epilepsy","volume":"60","author":"Abasi","year":"2019","journal-title":"Epilepsia"},{"key":"10.1016\/j.yebeh.2024.109744_b0015","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"3","key":"10.1016\/j.yebeh.2024.109744_b0020","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3390\/computation11030052","article-title":"Theoretical understanding of convolutional neural network: concepts, architectures, applications, future directions","volume":"11","author":"Taye","year":"2023","journal-title":"Computation"},{"key":"10.1016\/j.yebeh.2024.109744_b0025","doi-asserted-by":"crossref","DOI":"10.35784\/jcsi.2727","article-title":"A comparison of conventional and deep learning methods of image classification","volume":"21","author":"Dovbnych","year":"2021","journal-title":"J Comput Sci Inst"},{"key":"10.1016\/j.yebeh.2024.109744_b0030","doi-asserted-by":"crossref","unstructured":"Liang, D., Jin, X., Yuan, Y., & Zou, R. (2023, February). Performance Analysis of Machine Learning Methods. In Journal of Physics: Conference Series (Vol. 2428, No. 1, p. 012039). IOP Publishing.","DOI":"10.1088\/1742-6596\/2428\/1\/012039"},{"issue":"22","key":"10.1016\/j.yebeh.2024.109744_b0035","doi-asserted-by":"crossref","first-page":"4712","DOI":"10.3390\/rs13224712","article-title":"Review of image classification algorithms based on convolutional neural networks","volume":"13","author":"Chen","year":"2021","journal-title":"Remote Sens (Basel)"},{"issue":"7","key":"10.1016\/j.yebeh.2024.109744_b0040","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/S0387-7604(01)00284-4","article-title":"Neurophysiology of spasms","volume":"23","author":"Vigevano","year":"2001","journal-title":"Brain Develop"},{"key":"10.1016\/j.yebeh.2024.109744_b0045","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.seizure.2016.11.021","article-title":"Epileptic spasms\u2014175 Years on: Trying to teach an old dog new tricks","volume":"44","author":"Wilmshurst","year":"2017","journal-title":"Seizure"},{"key":"10.1016\/j.yebeh.2024.109744_b0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.yebeh.2020.107531","article-title":"Three different Scenarios for epileptic spasms","volume":"113","author":"Fusco","year":"2020","journal-title":"Epilepsy Behav"},{"issue":"3","key":"10.1016\/j.yebeh.2024.109744_b0055","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/ab0ab5","article-title":"Deep learning for electroencephalogram (EEG) classification tasks: A review","volume":"16","author":"Craik","year":"2019","journal-title":"J Neural Eng"},{"issue":"5","key":"10.1016\/j.yebeh.2024.109744_b0060","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/ab260c","article-title":"Deep learning-based electroencephalography analysis: a systematic review","volume":"16","author":"Roy","year":"2019","journal-title":"J Neural Eng"},{"key":"10.1016\/j.yebeh.2024.109744_b0065","doi-asserted-by":"crossref","unstructured":"Antoniades, A., Spyrou, L., Took, C. C., & Sanei, S. (2016, September). Deep learning for epileptic intracranial EEG data. In2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)(pp. 1-6). IEEE.","DOI":"10.1109\/MLSP.2016.7738824"},{"key":"10.1016\/j.yebeh.2024.109744_b0070","unstructured":"Asif, U., Roy, S., Tang, J., & Harrer, S. (2019). SeizureNet: a deep convolutional neural network for accurate seizure type classification and seizure detection.arXiv preprint arXiv:1903.03232."},{"key":"10.1016\/j.yebeh.2024.109744_b0075","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.eswa.2018.04.021","article-title":"An automated system for epilepsy detection using EEG brain signals based on deep learning approach","volume":"107","author":"Ullah","year":"2018","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.yebeh.2024.109744_b0080","doi-asserted-by":"crossref","unstructured":"Bouaziz, B., Chaari, L., Batatia, H., & Quintero-Rinc\u00f3n, A. (2019). Epileptic Seizure Detection Using a Convolutional Neural Network. InDigital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine(pp. 79-86). Springer, Cham.","DOI":"10.1007\/978-3-030-11800-6_9"},{"issue":"2","key":"10.1016\/j.yebeh.2024.109744_b0085","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1109\/JBHI.2019.2933046","article-title":"Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network","volume":"24","author":"Zhang","year":"2019","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.yebeh.2024.109744_b0090","article-title":"Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images","volume":"22","author":"Emami","year":"2019","journal-title":"NeuroImage: Clinical"},{"key":"10.1016\/j.yebeh.2024.109744_b0095","unstructured":"Bashivan, P., Rish, I., Yeasin, M. & Codella, N. (2016). Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks. In Y. Bengio & Y. LeCun (eds.), ICLR."},{"key":"10.1016\/j.yebeh.2024.109744_b0100","doi-asserted-by":"crossref","unstructured":"Bashivan, Pouya, Yeasin, Mohammed, and M., Bidelman Gavin. Single trial prediction of normal and excessive cognitive load through EEG feature fusion. Proceedings of IEEE Signal Processing in Medicine and Biology (SPMB) conference, pp. 1\u20135, December 2015.","DOI":"10.1109\/SPMB.2015.7405422"},{"key":"10.1016\/j.yebeh.2024.109744_b0105","doi-asserted-by":"crossref","unstructured":"Cochran, W.T., Cooley, J.W., Favin, D.L., Helms, H.D., Kaenel, R.A., Lang, W.W., Maling, G.C., Nelson, D.E., Rader, C.M., & Welch, P.D. (1967). What is the fast Fourier transform.","DOI":"10.1109\/PROC.1967.5957"},{"key":"10.1016\/j.yebeh.2024.109744_b0110","unstructured":"LeCun, Y., Boser, B. E., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. E., & Jackel, L. D. (1990). Handwritten digit recognition with a back-propagation network. InAdvances in neural information processing systems(pp. 396-404)."},{"key":"10.1016\/j.yebeh.2024.109744_b0115","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. InAdvances in neural information processing systems(pp. 1097-1105)."},{"key":"10.1016\/j.yebeh.2024.109744_b0120","unstructured":"Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR, abs\/1409.1556."},{"key":"10.1016\/j.yebeh.2024.109744_b0125","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. CoRR, abs\/1704.04861."},{"key":"10.1016\/j.yebeh.2024.109744_b0130","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. InProceedings of the IEEE conference on computer vision and pattern recognition(pp. 4700-4708).","DOI":"10.1109\/CVPR.2017.243"},{"key":"10.1016\/j.yebeh.2024.109744_b0135","first-page":"144","article-title":"A training algorithm for optimal margin classifiers","author":"Boser","year":"1992","journal-title":"Comput Learn Theory"},{"key":"10.1016\/j.yebeh.2024.109744_b0140","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.seizure.2016.11.021","article-title":"Epileptic spasms \u2013 175 years on: Trying to teach an old dog new tricks","volume":"44","author":"Wilmshurst","year":"2017","journal-title":"Seizure"},{"key":"10.1016\/j.yebeh.2024.109744_b0145","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.yebeh.2018.09.030","article-title":"Automated seizure prediction","volume":"88","author":"Acharya","year":"2018","journal-title":"Epilepsy Behav"},{"issue":"S1","key":"10.1016\/j.yebeh.2024.109744_b0150","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1111\/epi.14054","article-title":"Wearable devices for sudden unexpected death in epilepsy prevention","volume":"59","author":"Ryvlin","year":"2018","journal-title":"Epilepsia"}],"container-title":["Epilepsy & Behavior"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1525505024001252?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1525505024001252?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T23:14:43Z","timestamp":1714691683000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1525505024001252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":30,"alternative-id":["S1525505024001252"],"URL":"https:\/\/doi.org\/10.1016\/j.yebeh.2024.109744","relation":{},"ISSN":["1525-5050"],"issn-type":[{"value":"1525-5050","type":"print"}],"subject":[],"published":{"date-parts":[[2024,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"eDeeplepsy: An artificial neural framework to reveal different brain states in children with epileptic spasms","name":"articletitle","label":"Article Title"},{"value":"Epilepsy & Behavior","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.yebeh.2024.109744","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"109744"}}