{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T17:30:17Z","timestamp":1793813417949,"version":"4.6.1"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T00:00:00Z","timestamp":1636588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:p>In people with drug resistant epilepsy (DRE), seizures are unpredictable, often occurring with little or no warning. The unpredictability causes anxiety and much of the morbidity and mortality of seizures. In this work, 102 seizures of mesial temporal lobe onset were analyzed from 19 patients with DRE who had simultaneous intracranial EEG (iEEG) and scalp EEG as part of their surgical evaluation. The first aim of this paper was to develop machine learning models for seizure prediction and detection (i) using iEEG only, (ii) scalp EEG only and (iii) jointly analyzing both iEEG and scalp EEG. The second goal was to test if machine learning could detect a seizure on scalp EEG when that seizure was not detectable by the human eye (surface negative) but was seen in iEEG. The final question was to determine if the deep learning algorithm could correctly lateralize the seizure onset. The seizure detection and prediction problems were addressed jointly by training Deep Neural Networks (DNN) on 4 classes: non-seizure, pre-seizure, left mesial temporal onset seizure and right mesial temporal onset seizure. To address these aims, the classification accuracy was tested using two deep neural networks (DNN) against 3 different types of similarity graphs which used different time series of EEG data. The convolutional neural network (CNN) with the Waxman similarity graph yielded the highest accuracy across all EEG data (iEEG, scalp EEG and combined). Specifically, 1 second epochs of EEG were correctly assigned to their seizure, pre-seizure, or non-seizure category over 98% of the time. Importantly, the pre-seizure state was classified correctly in the vast majority of epochs (&amp;gt;97%). Detection from scalp EEG data alone of surface negative seizures and the seizures with the delayed scalp onset (the surface negative portion) was over 97%. In addition, the model accurately lateralized all of the seizures from scalp data, including the surface negative seizures. This work suggests that highly accurate seizure prediction and detection is feasible using either intracranial or scalp EEG data. Furthermore, surface negative seizures can be accurately predicted, detected and lateralized with machine learning even when they are not visible to the human eye.<\/jats:p>","DOI":"10.3389\/fneur.2021.705119","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T12:47:42Z","timestamp":1636634862000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":36,"title":["Deep Learning of Simultaneous Intracranial and Scalp EEG for Prediction, Detection, and Lateralization of Mesial Temporal Lobe Seizures"],"prefix":"10.3389","volume":"12","author":[{"given":"Zan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madeline","family":"Fields","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fedor","family":"Panov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saadi","family":"Ghatan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B\u00fclent","family":"Yener","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lara","family":"Marcuse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,11,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/S1474-4422(20)30396-3","article-title":"Forecasting seizure risk in adults with focal epilepsy: a development and validation study","volume":"20","author":"Proix","year":"2021","journal-title":"Lancet Neurol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1093\/brain\/awl241","article-title":"Seizure prediction: the long and winding road","volume":"130","author":"Mormann","year":"2007","journal-title":"Brain."},{"key":"B3","doi-asserted-by":"publisher","first-page":"S2","DOI":"10.1111\/epi.16541","article-title":"Seizure forecasting and cyclic control of seizures","volume":"62","author":"Stirling","year":"2021","journal-title":"Epilepsia."},{"key":"B4","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1038\/s41582-018-0055-2","article-title":"Seizure prediction \u2014 ready for a new era","volume":"14","author":"Kuhlmann","year":"2018","journal-title":"Nat Rev Neurol."},{"key":"B5","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1111\/epi.16333","article-title":"Machine learning applications in epilepsy","volume":"60","author":"Abbasi","year":"2019","journal-title":"Epilepsia."},{"key":"B6","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1109\/RBME.2020.3008792","article-title":"Machine learning for predicting epileptic seizures using EEG signals: a review","volume":"14","author":"Rasheed","year":"2021","journal-title":"IEEE Rev Biomed Eng."},{"key":"B7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s40708-020-00105-1","article-title":"A review of epileptic seizure detection using machine learning classifiers","volume":"7","author":"Siddiqui","year":"2020","journal-title":"Brain Inform."},{"key":"B8","doi-asserted-by":"publisher","first-page":"113788","DOI":"10.1016\/j.eswa.2020.113788","article-title":"A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals","volume":"163","author":"Shoeibi","year":"2021","journal-title":"Expert Syst Appl."},{"key":"B9","doi-asserted-by":"publisher","first-page":"5780","DOI":"10.3390\/ijerph18115780","article-title":"Epileptic seizure detection using deep learning techniques: a review","volume":"18","author":"Shoeibi","year":"2020","journal-title":"J Environ Res Public Health."},{"key":"B10","doi-asserted-by":"publisher","first-page":"i10","DOI":"10.1093\/bioinformatics\/btm210","article-title":"Multiway analysis of epilepsy tensors","volume":"23","author":"Acar","year":"2007","journal-title":"Bioinformatics."},{"key":"B11","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1109\/ICCKE48569.2019.8964826","article-title":"Epileptic seizures detection in EEG signals using TQWT and ensemble learning","author":"Ghassemi","year":"2019","journal-title":"2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)"},{"key":"B12","doi-asserted-by":"publisher","first-page":"e0228025","DOI":"10.1371\/journal.pone.0228025","article-title":"Predicting epileptic seizures using nonnegative matrix factorization","volume":"15","author":"Stojanovi\u0107","year":"2020","journal-title":"PLoS ONE."},{"key":"B13","doi-asserted-by":"publisher","first-page":"143999","DOI":"10.1109\/ACCESS.2019.2944691","article-title":"Epileptic seizure forecasting with generative adversarial networks","volume":"7","author":"Truong","year":"2019","journal-title":"IEEE Access."},{"key":"B14","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.yebeh.2015.03.010","article-title":"On the proper selection of pre-ictal period for seizure prediction","volume":"46","author":"Bandarabadi","year":"2015","journal-title":"Epilepsy Behav"},{"key":"B15","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.neunet.2018.04.018","article-title":"Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram","volume":"105","author":"Truong","year":"2018","journal-title":"Neural Netw."},{"key":"B16","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1097\/WCO.0000000000000429","article-title":"A forward-looking review of seizure prediction","volume":"30","author":"Freestone","year":"2017","journal-title":"Curr Opin Neurol."},{"key":"B17","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/S1474-4422(13)70075-9","article-title":"Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study","volume":"12","author":"Cook","year":"2013","journal-title":"Lancet Neurol"},{"key":"B18","doi-asserted-by":"publisher","first-page":"2109","DOI":"10.1109\/TBME.2017.2785401","article-title":"Focal onset seizure prediction using convolutional networks","volume":"65","author":"Khan","year":"2018","journal-title":"IEEE Trans Biomed Eng."},{"key":"B19","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1145\/2649387.2649423","article-title":"Graph-theoretic analysis of epileptic seizures on scalp EEG recordings","volume-title":"Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2014","author":"Dhulekar","year":"2014"},{"key":"B20","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1109\/49.12889","article-title":"Routing of multipoint connections","volume":"6","author":"Waxman","year":"1988","journal-title":"IEEE J Sel Area Commun."},{"key":"B21","first-page":"32","article-title":"Seizure prediction by graph mining transfer learning and transformation learning","author":"Dhulekar","year":"2015","journal-title":"Proc. Int. Conf. Mach. Learn. Data Mining"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1097\/WNP.0000000000000739","article-title":"The sensitivity of scalp EEG at detecting seizures-a simultaneous scalp and stereo EEG study","author":"Casale","year":"2020","journal-title":"J Clin Neurophysiol"},{"key":"B23","doi-asserted-by":"crossref","first-page":"2461","DOI":"10.1007\/s11517-019-02039-1","article-title":"Real-time epileptic seizure prediction based on online monitoring of pre-ictal features","volume":"57","author":"Sadeghzadeh","year":"2019","journal-title":"Medical and biological engineering and computing"},{"key":"B24","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compbiomed.2018.05.019","article-title":"M, Pezoulas VC, Zervakis M, Konitsiotis S, Koutsouris DD, Fotiadis DI. A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals","volume":"99","author":"Tsiouris","year":"2018","journal-title":"Comput Biol Med."},{"key":"B25","first-page":"1","article-title":"IoT based efficient epileptic seizure prediction system using deep learning","author":"Daoud","year":"2020","journal-title":"2020 IEEE 6th World Forum on Internet of Things (WF-IoT)"}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2021.705119\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T23:17:21Z","timestamp":1726096641000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2021.705119\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,11]]},"references-count":25,"alternative-id":["10.3389\/fneur.2021.705119"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2021.705119","relation":{},"ISSN":["1664-2295"],"issn-type":[{"value":"1664-2295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,11]]},"article-number":"705119"}}