{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T10:06:21Z","timestamp":1764842781508,"version":"3.41.2"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T00:00:00Z","timestamp":1695945600000},"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>The automatic precision detection technology based on electroencephalography (EEG) is essential in epilepsy studies. It can provide objective proof for epilepsy diagnosis, treatment, and evaluation, thus helping doctors improve treatment efficiency. At present, the normal and acute phases of epilepsy can be well identified through EEG analysis, but distinguishing between the normal and chronic phases is still tricky.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this paper, five popular complexity indicators of EEG signal, including approximate entropy, sample entropy, permutation entropy, fuzzy entropy and Kolmogorov complexity, are computed from rat hippocampi to characterize the normal, acute, and chronic phases during epileptogenesis. Results of one-way ANOVA and principal component analysis both show that utilizing complexity features, we are able to easily identify differences between normal, acute, and chronic phases. We also propose an innovative framework for epilepsy detection based on graph convolutional neural network (GCNN) using multi-channel EEG complexity as input.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Combining information of five complexity measures at eight channels, our GCNN model demonstrate superior ability in recognizing the normal, acute, and chronic phases. Experiments results show that our GCNN model reached the high prediction accuracy above 98% and F1 score above 97% among these three phases for each individual rat.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Our research practice based on real data shows that EEG complexity characteristics are of great significance for recognizing different stages of epilepsy.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fncom.2023.1211096","type":"journal-article","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T12:56:00Z","timestamp":1695992160000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages"],"prefix":"10.3389","volume":"17","author":[{"given":"Shiming","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaopei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panpan","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoling","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,9,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.patrec.2017.05.007","article-title":"Classification of focal and non-focal EEG using entropies","volume":"94","author":"Arunkumar","year":"2017","journal-title":"Pattern Recogn. Lett"},{"key":"B2","doi-asserted-by":"publisher","first-page":"174102","DOI":"10.1103\/PhysRevLett.88.174102","article-title":"Permutation entropy: a natural complexity measure for time series","volume":"88","author":"Bandt","year":"2002","journal-title":"Phys. Rev. Lett"},{"key":"B3","doi-asserted-by":"publisher","first-page":"101702","DOI":"10.1016\/j.bspc.2019.101702","article-title":"A review of feature extraction and performance evaluation in epileptic seizure detection using EEG","volume":"57","author":"Boonyakitanont","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"B4","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1038\/nrn2575","article-title":"Complex brain networks: graph theoretical analysis of structural and functional systems","volume":"10","author":"Bullmore","year":"2009","journal-title":"Nat. Rev. Neurosci"},{"key":"B5","doi-asserted-by":"publisher","first-page":"350359","DOI":"10.1147\/rd.214.0350","article-title":"Algorithmic information theory","volume":"21","author":"Chaitin","year":"1977","journal-title":"IBM J. Res. Dev"},{"key":"B6","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/TNSRE.2007.897025","article-title":"Characterization of surface EMG signal based on fuzzy entropy","volume":"15","author":"Chen","year":"2007","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng"},{"key":"B7","first-page":"160","article-title":"\u201cTemporal graph convolutional networks for automatic seizure detection,\u201d","author":"Covert","year":"2019","journal-title":"Proceedings of Machine Learning Research 106 (PMLR)"},{"key":"B8","doi-asserted-by":"publisher","first-page":"e0264537","DOI":"10.1371\/journal.pone.0264537","article-title":"Automated seizure activity tracking and onset zone localization from scalp EEG using deep neural networks","volume":"17","author":"Craley","year":"2022","journal-title":"PLoS ONE"},{"key":"B9","first-page":"3844","article-title":"\u201cConvolutional neural networks on graphs with fast localized spectral filtering,\u201d","volume-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS'16","author":"Defferrard","year":"2016"},{"key":"B10","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1142\/S0129065710002334","article-title":"Automatic identification of epileptic and background EEG signals using frequency domain parameters","volume":"20","author":"Faust","year":"2010","journal-title":"Int. J. Neural Syst"},{"key":"B11","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1111\/j.0013-9580.2005.66104.x","article-title":"Epileptic seizures and epilepsy: definitions proposed by the international league against epilepsy (ILAE) and the international bureau for epilepsy (IBE)","volume":"46","author":"Fisher","year":"2005","journal-title":"Epilepsia"},{"key":"B12","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3390\/econometrics7010010","article-title":"Permutation entropy and information recovery in nonlinear dynamic economic time series","volume":"7","author":"Henry","year":"2019","journal-title":"Econometrics"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TNNLS.2022.3202569","article-title":"GCNs-Net: a graph convolutional neural network approach for decoding time-resolved EEG motor imagery signals","author":"Hou","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1324","DOI":"10.24963\/ijcai.2020\/184","article-title":"\u201cGraphsleepnet: adaptive spatial-temporal graph convolutional networks for sleep stage classification,\u201d","author":"Jia","year":"2020","journal-title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), Vol. 2021"},{"key":"B15","doi-asserted-by":"publisher","first-page":"e27196","DOI":"10.1371\/journal.pone.0027196","article-title":"Redistribution of cb1 cannabinoid receptors in the acute and chronic phases of pilocarpine-induced epilepsy","volume":"6","author":"Karl\u00f3cai","year":"2011","journal-title":"PLoS ONE"},{"key":"B16","doi-asserted-by":"publisher","first-page":"102201","DOI":"10.1016\/j.artmed.2021.102201","article-title":"Seizure detection from multi-channel EEG using entropy-based dynamic graph embedding","volume":"122","author":"Li","year":"2021","journal-title":"Artif. Intell. Med"},{"key":"B17","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.bspc.2017.01.010","article-title":"Automatic epileptic EEG detection using DT-CWT-based non-linear features","volume":"34","author":"Li","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"B18","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-49820-1","volume-title":"An Introduction to Kolmogorov Complexity and Its Applications, Vol. 3","author":"Li","year":"2008"},{"key":"B19","doi-asserted-by":"publisher","first-page":"035004","DOI":"10.1088\/1741-2552\/ab909d","article-title":"Learning graph in graph convolutional neural networks for robust seizure prediction","volume":"17","author":"Lian","year":"2020","journal-title":"J. Neural Eng"},{"key":"B20","doi-asserted-by":"publisher","first-page":"16","DOI":"10.3389\/fncom.2015.00016","article-title":"EEG entropy measures in anesthesia","volume":"9","author":"Liang","year":"2015","journal-title":"Front. Comput. Neurosci"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1475-925X-3-7","article-title":"Nonlinear analysis of EEG signals at different mental states","volume":"3","author":"Natarajan","year":"2004","journal-title":"Biomed. Eng. Online"},{"key":"B22","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/BF01619355","article-title":"A regularity statistic for medical data analysis","volume":"7","author":"Pincus","year":"1991","journal-title":"J. Clin. Monit"},{"key":"B23","doi-asserted-by":"publisher","first-page":"106950","DOI":"10.1016\/j.cmpb.2022.106950","article-title":"A graph convolutional neural network for the automated detection of seizures in the neonatal EEG","volume":"2022","author":"Raeisi","year":"2022","journal-title":"Comput. Methods Prog. Biomed"},{"key":"B24","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. Physiol. Heart Circul. Physiol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1002\/epi4.12010","article-title":"Defeating epilepsy: a global public health commitment","volume":"2","author":"Saxena","year":"2017","journal-title":"Epileps. Open"},{"key":"B26","doi-asserted-by":"publisher","first-page":"669","DOI":"10.3390\/e17020669","article-title":"Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals","volume":"17","author":"Sharma","year":"2014","journal-title":"Entropy"},{"key":"B27","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1080\/03091902.2018.1464075","article-title":"Effect of filtering with time domain features for the detection of epileptic seizure from EEG signals","volume":"42","author":"Sharmila","year":"2018","journal-title":"J. Med. Eng. Technol"},{"volume-title":"A Preliminary Report on a General Theory of Inductive Inference","year":"1960","author":"Solomonoff","key":"B28"},{"key":"B29","doi-asserted-by":"publisher","first-page":"204","DOI":"10.3389\/fneur.2016.00204","article-title":"Dynamic changes in spectral and spatial signatures of high frequency oscillations in rat hippocampi during epileptogenesis in acute and chronic stages","volume":"7","author":"Song","year":"2016","journal-title":"Front. Neurol"},{"key":"B30","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","article-title":"EEG emotion recognition using dynamical graph convolutional neural networks","volume":"11","author":"Song","year":"2018","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B31","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10916-005-6133-1","article-title":"Artificial neural network based epileptic detection using time-domain and frequency-domain features","volume":"29","author":"Srinivasan","year":"2005","journal-title":"J. Med. Syst"},{"key":"B32","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1109\/JSTSP.2017.2726981","article-title":"Robust spatial filtering with graph convolutional neural networks","volume":"11","author":"Such","year":"2017","journal-title":"IEEE J. Select. Top. Signal Process"},{"key":"B33","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/TITB.2009.2017939","article-title":"Epileptic seizure detection in EEGs using time-frequency analysis","volume":"13","author":"Tzallas","year":"2009","journal-title":"IEEE transactions on information technology in biomedicine"},{"key":"B34","first-page":"367","article-title":"\u201cEEG-GCNN: augmenting electroencephalogram-based neurological disease diagnosis using a domain-guided graph convolutional neural network,\u201d","author":"Wagh","year":"2020","journal-title":"Proceedings of Machine Learning Research 136 (PMLR)"},{"key":"B35","doi-asserted-by":"publisher","first-page":"222","DOI":"10.3390\/e19060222","article-title":"Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis","volume":"19","author":"Wang","year":"2017","journal-title":"Entropy"},{"key":"B36","doi-asserted-by":"publisher","first-page":"101551","DOI":"10.1016\/j.bspc.2019.04.028","article-title":"Automatic epileptic EEG detection using convolutional neural network with improvements in time-domain","volume":"53","author":"Wei","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"B37","doi-asserted-by":"publisher","first-page":"6879","DOI":"10.1097\/MD.0000000000006879","article-title":"Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification","volume":"96","author":"Wen","year":"2017","journal-title":"Medicine"},{"key":"B38","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":"B39","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.eplepsyres.2011.04.013","article-title":"Epileptic EEG classification based on extreme learning machine and nonlinear features","volume":"96","author":"Yuan","year":"2011","journal-title":"Epilepsy Res"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1142\/2895","author":"Zadeh","year":"1996","journal-title":"Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers, Vol. 6"},{"key":"B41","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1109\/TAFFC.2019.2937768","article-title":"GCB-Net: graph convolutional broad network and its application in emotion recognition","volume":"13","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Affect. Comput"}],"container-title":["Frontiers in Computational Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2023.1211096\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T12:56:16Z","timestamp":1695992176000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2023.1211096\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,29]]},"references-count":41,"alternative-id":["10.3389\/fncom.2023.1211096"],"URL":"https:\/\/doi.org\/10.3389\/fncom.2023.1211096","relation":{},"ISSN":["1662-5188"],"issn-type":[{"type":"electronic","value":"1662-5188"}],"subject":[],"published":{"date-parts":[[2023,9,29]]},"article-number":"1211096"}}