{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T14:28:28Z","timestamp":1783952908274,"version":"3.55.0"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T00:00:00Z","timestamp":1683158400000},"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:p>Epilepsy is the second common neurological disorder after headache, accurate and reliable prediction of seizures is of great clinical value. Most epileptic seizure prediction methods consider only the EEG signal or extract and classify the features of EEG and ECG signals separately, the improvement of prediction performance from multimodal data is not fully considered. In addition, epilepsy data are time-varying, with differences between each episode in a patient, making it difficult for traditional curve-fitting models to achieve high accuracy and reliability. In order to improve the accuracy and reliability of the prediction system, we propose a novel personalized approach based on data fusion and domain adversarial training to predict epileptic seizures using leave-one-out cross-validation, which achieves an average accuracy, sensitivity and specificity of 99.70, 99.76, and 99.61%, respectively, with an average error alarm rate (FAR) of 0.001. Finally, the advantage of this approach is demonstrated by comparison with recent relevant literature. This method will be incorporated into clinical practice to provide personalized reference information for epileptic seizure prediction.<\/jats:p>","DOI":"10.3389\/fncom.2023.1172987","type":"journal-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T04:13:38Z","timestamp":1683173618000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Patient-specific approach using data fusion and adversarial training for epileptic seizure prediction"],"prefix":"10.3389","volume":"17","author":[{"given":"Yong","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolin","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoguang","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,5,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/0013-4694(87)90093-9","article-title":"Firing patterns of human limbic neurons during stereoencephalography (SEEG) and clinical temporal lobe seizures.","volume":"66","author":"Babb","year":"1987","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.inffus.2013.10.011","article-title":"Image fusion using intuitionistic fuzzy sets.","volume":"20","author":"Balasubramaniam","year":"2014","journal-title":"Inf. Fusion"},{"key":"B3","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/ISCE.2019.8900998","article-title":"Epileptic seizures prediction based on the combination of EEG and ECG for the application in a wearable device","author":"Billeci","year":"2019","journal-title":"2019 IEEE 23rd International symposium on consumer technologies (ISCT)"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102963","article-title":"Patient-specific method of sleep electroencephalography using wavelet packet transform and Bi-LSTM for epileptic seizure prediction.","volume":"70","author":"Cheng","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.1002\/acn3.51382","article-title":"The brain-heart interaction in epilepsy: Implications for diagnosis, therapy, and SUDEP prevention.","volume":"8","author":"Costagliola","year":"2021","journal-title":"Ann. Clin. Transl. Neurol."},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.101720","article-title":"Epileptic seizure prediction by the detection of seizure waveform from the pre-ictal phase of EEG signal.","volume":"57","author":"Das","year":"2020","journal-title":"Biomed. Signal Process. Control."},{"key":"B7","doi-asserted-by":"publisher","first-page":"1494","DOI":"10.1109\/TBME.2018.2874716","article-title":"A patient-specific approach for short-term epileptic seizures prediction through the analysis of EEG synchronization.","volume":"66","author":"Detti","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.3390\/pr8070846","article-title":"EEG synchronization analysis for seizure prediction: A study on data of noninvasive recordings.","volume":"8","author":"Detti","year":"2020","journal-title":"Processes"},{"key":"B9","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1111\/epi.13670","article-title":"Operational classification of seizure types by the international league against epilepsy: Position paper of the ILAE commission for classification and terminology.","volume":"58","author":"Fisher","year":"2017","journal-title":"Epilepsia"},{"key":"B10","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.48550\/arXiv.1505.07818","article-title":"Domain-adversarial training of neural networks.","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"B11","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1049\/cp:19991218","article-title":"Learning to forget: Continual prediction with LSTM","author":"Gers","year":"1999","journal-title":"1999 Ninth international conference on artificial neural networks ICANN 99"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/STSIVA.2016.7743357","article-title":"Automatic epileptic seizure prediction based on scalp EEG and ECG signals","author":"Hoyos-Osorio","year":"2016","journal-title":"2016 XXI symposium on signal processing, images and artificial vision (STSIVA)"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102767","article-title":"Deep learning based efficient epileptic seizure prediction with EEG channel optimization.","volume":"68","author":"Jana","year":"","journal-title":"Biomed. Signal Process. Control"},{"key":"B14","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/978-981-16-5078-9_20","article-title":"Epileptic seizure prediction from raw EEG signal using convolutional neural network.","volume":"796","author":"Jana","year":"","journal-title":"Mach. Vis. Augment.Intell. Theory Appl."},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/708075","article-title":"MRI and PET image fusion using fuzzy logic and image local features.","volume":"2014","author":"Javed","year":"2014","journal-title":"Sci. World J."},{"key":"B16","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":"B17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3373017.3373055","article-title":"Epileptic seizure detection using convolutional neural network: A multi-biosignal study","author":"Liu","year":"2020","journal-title":"Proceedings of the Australasian computer science week multiconference 2020, ACSW 2020"},{"key":"B18","doi-asserted-by":"publisher","first-page":"11749","DOI":"10.1609\/AAAI.V34I07.6846","article-title":"Domain generalization using a mixture of multiple latent domains.","volume":"34","author":"Matsuura","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"B19","doi-asserted-by":"publisher","first-page":"3","DOI":"10.4149\/BLL_2017_001","article-title":"Heart rate variability as a biomarker for epilepsy seizure prediction.","volume":"118","author":"Moridani","year":"2017","journal-title":"Bratisl Lek Listy"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1809.02176","article-title":"Multi-adversarial domain adaptation","author":"Pei","year":"2018","journal-title":"AAAI.Proceedings of AAAI conference on artificial intelligence"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/BMEiCON.2014.7017433","article-title":"The preliminary study of EEG and ECG for epileptic seizure prediction based on Hilbert Huang Transform","author":"Phomsiricharoenphant","year":"2014","journal-title":"The 7th 2014 biomedical engineering international conference"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.3390\/jpm11101028","article-title":"Automated epileptic seizure detection in pediatric subjects of CHB-MIT EEG database-a survey.","volume":"11","author":"Prasanna","year":"2021","journal-title":"J. Pers. Med."},{"key":"B23","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1038\/nrneurol.2009.80","article-title":"Epileptogenesis in the immature brain: Emerging mechanisms.","volume":"5","author":"Rakhade","year":"2009","journal-title":"Nat. Rev. Neurol. J."},{"key":"B24","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":"B25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICOSEC51865.2021.9591882","article-title":"Ensemble classification for epileptic seizure prediction","author":"Saranya","year":"2021","journal-title":"2021 2nd international conference on smart electronics and communication (ICOSEC)"},{"key":"B26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CFIS54774.2022.9756421","article-title":"ECG-based prediction of epileptic seizures using machine learning methods","author":"Seifi","year":"2022","journal-title":"2022 9th Iranian joint congress on fuzzy and intelligent systems (CFIS)"},{"key":"B27","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/978-3-030-98886-9_23","article-title":"Epileptic seizure detection using continuous wavelet transform and deep neural networks.","volume":"886","author":"Shukla","year":"2022","journal-title":"Lect. Notes Electr. Eng."},{"key":"B28","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/23.589532","article-title":"Importance of input data normalization for the application of neural networks to complex industrial problem.","volume":"44","author":"Sola","year":"1997","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"B29","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compbiomed.2018.05.019","article-title":"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":"B30","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1016\/j.clinph.2020.01.007","article-title":"Electrocardiographic changes associated with epilepsy beyond heart rate and their utilization in future seizure detection and forecasting methods.","volume":"131","author":"Ufongene","year":"2020","journal-title":"Clin. Neurophysiol."},{"key":"B31","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.bbe.2021.01.001","article-title":"Epileptic seizure prediction using scalp electroencephalogram signals.","volume":"41","author":"Usman","year":"2021","journal-title":"Biocybern. Biomed. Eng."},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.3390\/s22114232","article-title":"A fuzzy similarity-based approach to classify numerically simulated and experimentally detected carbon fiber-reinforced polymer plate defects.","volume":"22","author":"Versaci","year":"2022","journal-title":"Sensors"},{"key":"B33","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/ICDM.2019.00088","article-title":"Transfer learning with dynamic adversarial adaptation network","author":"Yu","year":"2019","journal-title":"IEEE international conference on data mining (ICDM)"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065717500435","article-title":"Epileptic seizure prediction using diffusion distance and Bayesian linear discriminate analysis on intracranial EEG.","volume":"28","author":"Yuan","year":"2018","journal-title":"Int. J. Neural Syst."},{"key":"B35","doi-asserted-by":"publisher","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 Process. Control"},{"key":"B36","doi-asserted-by":"crossref","DOI":"10.1016\/j.jneumeth.2019.108447","article-title":"Establishing functional brain networks using nonlinear partial directed coherence method to predict epileptic seizures.","volume":"329","author":"Zhang","year":"2020","journal-title":"J. Neurosci. Methods"}],"container-title":["Frontiers in Computational Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2023.1172987\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T04:13:42Z","timestamp":1683173622000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2023.1172987\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,4]]},"references-count":36,"alternative-id":["10.3389\/fncom.2023.1172987"],"URL":"https:\/\/doi.org\/10.3389\/fncom.2023.1172987","relation":{},"ISSN":["1662-5188"],"issn-type":[{"value":"1662-5188","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,4]]},"article-number":"1172987"}}