{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T14:25:37Z","timestamp":1769783137298,"version":"3.49.0"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T00:00:00Z","timestamp":1720656000000},"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. Comput. Neurosci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Epilepsy is a common neurological condition that affects a large number of individuals worldwide. One of the primary challenges in epilepsy is the accurate and timely detection of seizure. Recently, the graph regularized broad learning system (GBLS) has achieved superior performance improvement with its flat structure and less time-consuming training process compared to deep neural networks. Nevertheless, the number of feature and enhancement nodes in GBLS is predetermined. These node settings are also randomly selected and remain unchanged throughout the training process. The characteristic of randomness is thus more easier to make non-optimal nodes generate, which cannot contribute significantly to solving the optimization problem.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>To obtain more optimal nodes for optimization and achieve superior automatic detection performance, we propose a novel broad neural network named self-adaptive evolutionary graph regularized broad learning system (SaE-GBLS). Self-adaptive evolutionary algorithm, which can construct mutation strategies in the strategy pool based on the experience of producing solutions for selecting network parameters, is incorporated into SaE-GBLS model for optimizing the node parameters. The epilepsy seizure is automatic detected by our proposed SaE-GBLS model based on three publicly available EEG datasets and one private clinical EEG dataset.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results and discussion<\/jats:title><jats:p>The experimental results indicate that our suggested strategy has the potential to perform as well as current machine learning approaches.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fncom.2024.1379368","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T04:52:08Z","timestamp":1720673528000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["SaE-GBLS: an effective self-adaptive evolutionary optimized graph-broad model for EEG-based automatic epileptic seizure detection"],"prefix":"10.3389","volume":"18","author":[{"given":"Liming","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Jiaqi","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Junwei","family":"Duan","sequence":"additional","affiliation":[]},{"given":"Yuhang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chun","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jingxin","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Zhiguo","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yujuan","family":"Quan","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781139103992","volume-title":"Introduction to Epilepsy","author":"Alarc\u00f3n","year":"2012"},{"key":"B2","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s13246-015-0333-x","article-title":"Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques","volume":"38","author":"Amin","year":"2015","journal-title":"Aust. Phys. Eng. Sci. Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"061907","DOI":"10.1103\/PhysRevE.64.061907","article-title":"Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state","volume":"64","author":"Andrzejak","year":"2001","journal-title":"Phys. Rev. E"},{"key":"B4","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/TNNLS.2017.2716952","article-title":"Broad learning system: an effective and efficient incremental learning system without the need for deep architecture","volume":"29","author":"Chen","year":"2017","journal-title":"IEEE Transact. Neural Netw. Learn. Syst"},{"key":"B5","doi-asserted-by":"publisher","first-page":"e0173138","DOI":"10.1371\/journal.pone.0173138","article-title":"A high-performance seizure detection algorithm based on discrete wavelet transform (DWT) and EEG","volume":"12","author":"Chen","year":"2017","journal-title":"PLoS ONE"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1945","DOI":"10.1007\/s11682-019-00138-z","article-title":"Voxel-based morphometry analysis and machine learning based classification in pediatric mesial temporal lobe epilepsy with hippocampal sclerosis","volume":"14","author":"Chen","year":"2020","journal-title":"Brain Imaging Behav"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40708-021-00123-7","article-title":"Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals","volume":"8","author":"Ein Shoka","year":"2021","journal-title":"Brain Inform"},{"key":"B8","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S1059-1311(98)90002-4","article-title":"Epilepsy and learning disabilities\u2014a challenge for the next millennium?","volume":"7","author":"Hannah","year":"1998","journal-title":"Seizure"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3241056","article-title":"Applying deep learning for epilepsy seizure detection and brain mapping visualization","volume":"15","author":"Hossain","year":"2019","journal-title":"ACM Transact. Multim. Comp. Commun. Appl"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11432-017-9421-3","article-title":"Discriminative graph regularized broad learning system for image recognition","volume":"61","author":"Jin","year":"2018","journal-title":"Sci. China Inf. Sci"},{"key":"B11","unstructured":"American Epilepsy Society Seizure Prediction Challenge2014"},{"key":"B12","doi-asserted-by":"publisher","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","article-title":"EEGNet: a compact convolutional neural network for eeg-based brain-computer interfaces","volume":"15","author":"Lawhern","year":"2018","journal-title":"J. Neural Eng"},{"key":"B13","volume-title":"Differential Evolution: A Practical Approach to Global Optimization","author":"Price","year":"2006"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-30504-7_8","article-title":"Differential evolution","author":"Price","year":"2013","journal-title":"Handbook of Optimization. Intelligent Systems Reference Library, Vol. 38"},{"key":"B15","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1109\/MAP.2011.5773566","article-title":"Differential evolution as applied to electromagnetics","volume":"53","author":"Rocca","year":"2011","journal-title":"IEEE Antennas Propagat. Mag"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1007\/s40815-018-0455-x","article-title":"Dual-tree complex wavelet transform-based features for automated alcoholism identification","volume":"20","author":"Sharma","year":"2018","journal-title":"Int. J. Fuzzy Syst"},{"key":"B17","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1097\/RLU.0000000000004072","article-title":"Machine learning quantitative analysis of FDG pet images of medial temporal lobe epilepsy patients","volume":"47","author":"Shih","year":"2022","journal-title":"Clin. Nucl. Med"},{"key":"B18","unstructured":"ShoebA. H.\n          AmericaDepartment Harvard University\u2013MIT Division of Health Sciences and TechnologyApplication of Machine Learning to Epileptic Seizure Onset Detection and Treatment2009"},{"key":"B19","doi-asserted-by":"publisher","first-page":"103417","DOI":"10.1016\/j.bspc.2021.103417","article-title":"Detection of epileptic seizures on eeg signals using anfis classifier, autoencoders and fuzzy entropies","volume":"73","author":"Shoeibi","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"B20","doi-asserted-by":"publisher","first-page":"5595","DOI":"10.1007\/s00521-018-3381-9","article-title":"A novel quick seizure detection and localization through brain data mining on ECoG dataset","volume":"31","author":"Siddiqui","year":"2019","journal-title":"Neural Comp. Appl"},{"key":"B21","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/NAFIPS.1996.534789","article-title":"On the usage of differential evolution for function optimization","volume-title":"Proceedings of North American Fuzzy Information Processing","author":"Storn","year":"1996"},{"key":"B22","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-19823-7_27","article-title":"Localization of epileptic foci by using convolutional neural network based on iEEG","volume-title":"Artificial Intelligence Applications and Innovations. AIAI 2019. IFIP Advances in Information and Communication Technology, Vol. 559","author":"Sui","year":"2019"},{"key":"B23","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1109\/CRCSIT.2017.7965539","article-title":"Classification and discrimination of focal and non-focal EEG signals based on deep neural network","volume-title":"2017 International Conference on Current Research in Computer Science and Information Technology (ICCIT)","author":"Taqi","year":"2017"},{"key":"B24","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":"Usman","year":"2021","journal-title":"Comput. Biol. Med"}],"container-title":["Frontiers in Computational Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2024.1379368\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T04:52:17Z","timestamp":1720673537000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2024.1379368\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,11]]},"references-count":24,"alternative-id":["10.3389\/fncom.2024.1379368"],"URL":"https:\/\/doi.org\/10.3389\/fncom.2024.1379368","relation":{},"ISSN":["1662-5188"],"issn-type":[{"value":"1662-5188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,11]]},"article-number":"1379368"}}