{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T13:05:16Z","timestamp":1784639116045,"version":"3.55.0"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T00:00:00Z","timestamp":1710806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171073"],"award-info":[{"award-number":["62171073"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62311530103"],"award-info":[{"award-number":["62311530103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106032"],"award-info":[{"award-number":["62106032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:p>Epileptic seizures are characterized by their sudden and unpredictable nature, posing significant risks to a patient\u2019s daily life. Accurate and reliable seizure prediction systems can provide alerts before a seizure occurs, as well as give the patient and caregivers provider enough time to take appropriate measure. This study presents an effective seizure prediction method based on deep learning that combine with handcrafted features. The handcrafted features were selected by Max-Relevance and Min-Redundancy (mRMR) to obtain the optimal set of features. To extract the epileptic features from the fused multidimensional structure, we designed a P3D-BiConvLstm3D model, which is a combination of pseudo-3D convolutional neural network (P3DCNN) and bidirectional convolutional long short-term memory 3D (BiConvLstm3D). We also converted EEG signals into a multidimensional structure that fused spatial, manual features, and temporal information. The multidimensional structure is then fed into a P3DCNN to extract spatial and manual features and feature-to-feature dependencies, followed by a BiConvLstm3D input to explore temporal dependencies while preserving the spatial features, and finally, a channel attention mechanism is implemented to emphasize the more representative information in the multichannel output. The proposed has an average accuracy of 98.13%, an average sensitivity of 98.03%, an average precision of 98.30% and an average specificity of 98.23% for the CHB-MIT scalp EEG database. A comparison of the proposed model with other baseline methods was done to confirm the better performance of features through time\u2013space nonlinear feature fusion. The results show that the proposed P3DCNN-BiConvLstm3D-Attention3D method for epilepsy prediction by time\u2013space nonlinear feature fusion is effective.<\/jats:p>","DOI":"10.3389\/fninf.2024.1354436","type":"journal-article","created":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T13:14:26Z","timestamp":1710854066000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN"],"prefix":"10.3389","volume":"18","author":[{"given":"Xin","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xicheng","family":"Lou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haohuan","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhangyong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisha","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,3,19]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.knosys.2013.02.014","article-title":"Automated EEG analysis of epilepsy: a review","volume":"45","author":"Acharya","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1166\/jmihi.2016.1736","article-title":"Automatic detection of epileptic seizures using new entropy measures","volume":"6","author":"Arunkumar","year":"2016","journal-title":"J. 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Phenom."},{"key":"ref9","author":"Hu","year":"2018"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"112420","DOI":"10.1016\/j.oceaneng.2022.112420","article-title":"3D wave simulation based on a deep learning model for spatiotemporal prediction","volume":"263","author":"Li","year":"2022","journal-title":"Ocean Eng."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"111032","DOI":"10.1016\/j.chaos.2021.111032","article-title":"Detection and classification of epileptic EEG signals by the methods of nonlinear dynamics","volume":"151","author":"Lu","year":"2021","journal-title":"Chaos Solitons Fractals"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"39998","DOI":"10.1109\/access.2020.2976866","article-title":"Epileptic seizures prediction using deep learning techniques","volume":"8","author":"Muhammad Usman","year":"2020","journal-title":"IEEE Access"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"104710","DOI":"10.1016\/j.compbiomed.2021.104710","article-title":"A deep learning based ensemble learning method for epileptic seizure prediction","volume":"136","author":"Muhammad Usman","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/tpami.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","article-title":"Approximate entropy as a measure of system complexity","volume":"88","author":"Pincus","year":"1991","journal-title":"Proc. Natl. Acad. Sci."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"114533","DOI":"10.1016\/j.eswa.2020.114533","article-title":"Dynamic learning framework for epileptic seizure prediction using sparsity based EEG reconstruction with optimized CNN classifier","volume":"170","author":"Prathaban","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref17","author":"Qiu","year":"2017"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Phys. Heart Circ. Phys."},{"key":"ref19","author":"Sharma","year":"2022"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"1940007","DOI":"10.1142\/s0219519419400074","article-title":"A novel approach for epilepsy detection using time\u2013frequency localized bi-orthogonal wavelet filter","volume":"19","author":"Sharma","year":"2019","journal-title":"J. Mech. Med. Biol."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"2405","DOI":"10.1007\/s40747-021-00627-z","article-title":"Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features","volume":"8","author":"Singh","year":"2022","journal-title":"Complex Intel. Syst."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1109\/titb.2006.884369","article-title":"Approximate entropy-based epileptic EEG detection using artificial neural networks","volume":"11","author":"Srinivasan","year":"2007","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref23","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":"ref24","doi-asserted-by":"publisher","first-page":"103462","DOI":"10.1016\/j.bspc.2021.103462","article-title":"Classification of epileptic seizures from electroencephalogram (EEG) data using bidirectional short-term memory (bi-LSTM) network architecture","volume":"73","author":"Tuncer","year":"2022","journal-title":"Biomed. Signal Proc. Control"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1109\/TMAG.2003.810365","article-title":"Fuzzy time series approach for disruption prediction in tokamak reactors","volume":"39","author":"Versaci","year":"2003","journal-title":"IEEE Trans. Magn."},{"key":"ref26","year":"2022"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.jneumeth.2015.01.015","article-title":"The detection of epileptic seizure signals based on fuzzy entropy","volume":"243","author":"Xiang","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"109624","DOI":"10.1016\/j.jneumeth.2022.109624","article-title":"Spatial-frequency-temporal convolutional recurrent network for olfactory-enhanced EEG emotion recognition","volume":"376","author":"Xing","year":"2022","journal-title":"J. Neurosci. Methods"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"2250032","DOI":"10.1142\/s0129065722500320","article-title":"Epileptic seizure prediction using deep neural networks via transfer learning and multi-feature fusion","volume":"32","author":"Yu","year":"2022","journal-title":"Int. J. Neural. Syst."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"3914","DOI":"10.1007\/s11227-020-03426-4","article-title":"A lightweight solution to epileptic seizure prediction based on EEG synchronization measurement","volume":"77","author":"Zhang","year":"2020","journal-title":"J. Supercomput."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"102293","DOI":"10.1016\/j.bspc.2020.102293","article-title":"Epilepsy prediction through optimized multidimensional sample entropy and bi-LSTM","volume":"64","author":"Zhang","year":"2021","journal-title":"Biomed. Signal Proc. 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