{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:08:35Z","timestamp":1791911315256,"version":"4.3.4"},"reference-count":43,"publisher":"IOP Publishing","issue":"6","license":[{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"tdm","delay-in-days":29,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62103301"],"award-info":[{"award-number":["62103301"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Neural Eng."],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Automatic detection and prediction of epilepsy are crucial for improving patient care and quality of life. However, existing methods typically focus on single-dimensional information and often confuse the periodic and aperiodic components in electrophysiological signals. <jats:italic>Approach.<\/jats:italic> We propose a novel deep learning (DL) framework that integrates temporal, spatial, and frequency information of EEG signals, in which periodic and aperiodic components are separated in the frequency domain. Specifically, we calculated the periodic and aperiodic components in single channel and the synchronization index of each component between channels. A self-attention mechanism is employed to filter single-channel features by selectively focusing on the most distinguishing features. Then, a hybrid bilinear DL network is utilized to capture the spatiotemporal features by combining a convolutional neural network and a long short-term memory network. Finally, a bilinear pooling layer is employed to extract second-order features based on interactions between these spatiotemporal features. <jats:italic>Main results.<\/jats:italic> The model achieves exceptional performance, with a detection accuracy of 98.84% on the CHB-MIT dataset, and a prediction accuracy of 98.44% on CHB-MIT and 97.65% on the Kaggle dataset, both with an false positive rate of 0.02. <jats:italic>Significance.<\/jats:italic> This work paves the way for developing real-time, wearable epilepsy prediction devices to improve patient care.<\/jats:p>","DOI":"10.1088\/1741-2552\/ada0e5","type":"journal-article","created":{"date-parts":[[2024,12,18]],"date-time":"2024-12-18T22:53:30Z","timestamp":1734562410000},"page":"066045","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Multi-dimensional hybrid bilinear CNN-LSTM models for epileptic seizure detection and prediction using EEG signals"],"prefix":"10.1088","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1846-7653","authenticated-orcid":true,"given":"Shan","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2189-8003","authenticated-orcid":true,"given":"Jiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3466-8573","authenticated-orcid":true,"given":"Lihui","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2024,12,30]]},"reference":[{"key":"jneada0e5bib1","doi-asserted-by":"publisher","first-page":"181","DOI":"10.51249\/hs.v3i03.1409","article-title":"Epilepsy: is it possible to have quality of life with epilepsy?","volume":"3","author":"Cunha","year":"2023","journal-title":"Health Soc."},{"key":"jneada0e5bib2","doi-asserted-by":"publisher","first-page":"24046","DOI":"10.1109\/ACCESS.2020.2970012","article-title":"Enhanced detection of epileptic seizure using EEG signals in combination with machine learning classifiers","volume":"8","author":"Mardini","year":"2020","journal-title":"IEEE Access"},{"key":"jneada0e5bib3","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.seizure.2023.04.012","article-title":"Personalized seizure detection using logistic regression machine learning based on wearable ECG-monitoring device","volume":"107","author":"Jeppesen","year":"2023","journal-title":"Seizure"},{"key":"jneada0e5bib4","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1109\/SMART55829.2022.10047389","article-title":"Epileptic seizure detection using machine learning: a review","author":"Sharma","year":"2022"},{"key":"jneada0e5bib5","doi-asserted-by":"publisher","first-page":"502","DOI":"10.14569\/IJACSA.2023.0140155","article-title":"Generalized epileptic seizure prediction using machine learning method","volume":"14","author":"Altaf","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"jneada0e5bib6","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/ICCES48960.2019.9068190","article-title":"A review of machine learning approaches for epileptic seizure prediction","author":"Selim","year":"2019"},{"key":"jneada0e5bib7","doi-asserted-by":"publisher","first-page":"409","DOI":"10.2217\/bmm-2018-0335","article-title":"Localizing epileptogenic regions using high-frequency oscillations and machine learning","volume":"13","author":"Weiss","year":"2019","journal-title":"Biomarker Med."},{"key":"jneada0e5bib8","doi-asserted-by":"publisher","first-page":"226","DOI":"10.9734\/ajrcos\/2024\/v17i6470","article-title":"EEG innovations in neurological disorder diagnostics: a five-year review","volume":"17","author":"Basak","year":"2024","journal-title":"Asian J. Res. Comput. Sci."},{"key":"jneada0e5bib9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"jneada0e5bib10","doi-asserted-by":"publisher","first-page":"7269","DOI":"10.3390\/s22197269","article-title":"A classification model of EEG signals based on RNN-LSTM for diagnosing focal and generalized epilepsy","volume":"22","author":"Najafi","year":"2022","journal-title":"Sensors"},{"key":"jneada0e5bib11","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 Intell. Syst."},{"key":"jneada0e5bib12","doi-asserted-by":"publisher","first-page":"6003","DOI":"10.3233\/JIFS-222745","article-title":"Epileptic seizure prediction and classification based on statistical features using LSTM fully connected neural network","volume":"44","author":"Goel","year":"2023","journal-title":"J. Int. Fuzzy Syst."},{"key":"jneada0e5bib13","doi-asserted-by":"publisher","DOI":"10.1002\/eng2.12827","article-title":"Enhancing EEG signals classification using LSTM\u2010CNN architecture","volume":"6","author":"Omar","year":"2024","journal-title":"Eng. Rep."},{"key":"jneada0e5bib14","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1007\/s11760-022-02318-9","article-title":"Automated epilepsy seizure detection from EEG signal based on hybrid CNN and LSTM model","volume":"17","author":"Pandey","year":"2022","journal-title":"Signal Image Video Process."},{"key":"jneada0e5bib15","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/ad3afd","article-title":"A hybrid 1D CNN-BiLSTM model for epileptic seizure detection using multichannel EEG feature fusion","volume":"10","author":"Ravi","year":"2024","journal-title":"Biomed. Phys. Eng. Express"},{"key":"jneada0e5bib16","doi-asserted-by":"publisher","first-page":"20605","DOI":"10.1007\/s00521-023-08832-2","article-title":"A CNN-LSTM hybrid network for automatic seizure detection in EEG signals","volume":"35","author":"Shanmugam","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"jneada0e5bib17","doi-asserted-by":"publisher","first-page":"57","DOI":"10.3233\/AIC-220177","article-title":"Automatic prediction of epileptic seizure using hybrid deep ResNet-LSTM model","volume":"36","author":"Singh","year":"2023","journal-title":"AI Commun."},{"key":"jneada0e5bib18","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1109\/JTEHM.2023.3290036","article-title":"An epileptic seizure prediction method based on CBAM-3D CNN-LSTM model","volume":"11","author":"Lu","year":"2023","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"jneada0e5bib19","doi-asserted-by":"publisher","first-page":"13613","DOI":"10.1523\/JNEUROSCI.2041-09.2009","article-title":"Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans","volume":"29","author":"Manning","year":"2009","journal-title":"J. Neurosci."},{"key":"jneada0e5bib20","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1038\/nrn3241","article-title":"The origin of extracellular fields and currents\u2014EEG, ECoG, LFP and spikes","volume":"13","author":"Buzs\u00e1ki","year":"2012","journal-title":"Nat. Rev. Neurosci."},{"key":"jneada0e5bib21","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1002655","article-title":"Human motor cortical sctivity is selectively phase-entrained on underlying rhythms","volume":"8","author":"Miller","year":"2012","journal-title":"PLoS Comput. Biol."},{"key":"jneada0e5bib22","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/j.cub.2013.05.001","article-title":"Asynchronous broadband signals are the principal source of the BOLD response in human visual cortex","volume":"23","author":"Winawer","year":"2013","journal-title":"Curr. Biol."},{"key":"jneada0e5bib23","doi-asserted-by":"publisher","DOI":"10.1101\/839258","article-title":"Co-increasing neuronal noise and beta power in the developing brain","author":"He","year":"2019"},{"key":"jneada0e5bib24","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1016\/j.bpsc.2020.02.004","article-title":"Memantine effects on electroencephalographic measures of putative excitatory\/inhibitory balance in schizophrenia","volume":"5","author":"Molina","year":"2020","journal-title":"Biol. Psychiatry Cogn. Neurosci. Neuroimaging"},{"key":"jneada0e5bib25","doi-asserted-by":"publisher","DOI":"10.1101\/2021.07.28.454235","article-title":"The timescale and magnitude of 1\/f aperiodic activity decrease with cortical depth in humans, macaques, and mice","author":"Halgren","year":"2021"},{"key":"jneada0e5bib26","first-page":"157","article-title":"Application of machine learning to epileptic seizure onset detection and treatment","author":"Shoeb","year":"2009"},{"key":"jneada0e5bib27","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.cir.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"circulation"},{"key":"jneada0e5bib28","doi-asserted-by":"publisher","DOI":"10.13026\/C2K01R","article-title":"CHB-MIT scalp EEG database (version 1.0. 0)","author":"Guttag","year":"2010"},{"key":"jneada0e5bib29","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":"jneada0e5bib30","author":"bbrinkm","year":"2014"},{"key":"jneada0e5bib31","doi-asserted-by":"publisher","first-page":"3884","DOI":"10.1109\/TNSRE.2023.3317093","article-title":"Epileptic seizure detection and prediction in EEGs using power spectra density parameterization","volume":"31","author":"Liu","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneada0e5bib32","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1038\/s41593-020-00744-x","article-title":"Parameterizing neural power spectra into periodic and aperiodic components","volume":"23","author":"Donoghue","year":"2020","journal-title":"Nat. Neurosci."},{"key":"jneada0e5bib33","doi-asserted-by":"publisher","first-page":"5547","DOI":"10.1109\/TII.2021.3133307","article-title":"EEGWaveNet: multiscale CNN-based spatiotemporal feature extraction for EEG seizure detection","volume":"18","author":"Thuwajit","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"jneada0e5bib34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/tensymp54529.2022.9864345","article-title":"Statistical feature rich deep learning based epileptic seizure detection","author":"Dalal","year":"2022"},{"key":"jneada0e5bib35","doi-asserted-by":"publisher","first-page":"49172","DOI":"10.1109\/ACCESS.2023.3277634","article-title":"Less parameterization inception-based end to end CNN model for EEG seizure detection","volume":"11","author":"Shyu","year":"2023","journal-title":"IEEE Access"},{"key":"jneada0e5bib36","doi-asserted-by":"publisher","first-page":"6458","DOI":"10.3390\/s22176458","article-title":"An intelligent epileptic prediction system based on synchrosqueezed wavelet transform and multi-level feature CNN for smart healthcare IoT","volume":"22","author":"Song","year":"2022","journal-title":"Sensors"},{"key":"jneada0e5bib37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JTEHM.2022.3144037","article-title":"Pediatric seizure prediction in scalp EEG using a multi-scale neural network with dilated convolutions","volume":"10","author":"Gao","year":"2022","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"jneada0e5bib38","doi-asserted-by":"publisher","first-page":"4158","DOI":"10.3390\/app12094158","article-title":"Seizure prediction based on transformer using scalp electroencephalogram","volume":"12","author":"Yan","year":"2022","journal-title":"Appl. Sci."},{"key":"jneada0e5bib39","article-title":"Automatic seizure prediction using CNN and LSTM","author":"Bhattacharya","year":"2022"},{"key":"jneada0e5bib40","doi-asserted-by":"publisher","first-page":"62855","DOI":"10.1109\/ACCESS.2023.3287927","article-title":"A multi-channel feature fusion CNN-BI-LSTM epilepsy EEG classification and prediction model based on attention mechanism","volume":"11","author":"Ma","year":"2023","journal-title":"IEEE Access"},{"key":"jneada0e5bib41","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065723500545","article-title":"Epileptic seizure prediction using attention augmented convolutional network","volume":"33","author":"Liu","year":"2023","journal-title":"Int. J. Neural Syst."},{"key":"jneada0e5bib42","doi-asserted-by":"publisher","first-page":"1847","DOI":"10.1109\/TNSRE.2023.3260845","article-title":"A self-interpretable deep learning model for seizure prediction using a multi-scale prototypical part network","volume":"31","author":"Gao","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"jneada0e5bib43","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/AICAS48895.2020.9073988","article-title":"An end-to-end deep learning approach for epileptic seizure prediction","author":"Xu","year":"2020"}],"container-title":["Journal of Neural Engineering"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ada0e5","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ada0e5\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ada0e5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ada0e5\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ada0e5\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ada0e5\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ada0e5\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ada0e5\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T10:54:58Z","timestamp":1735556098000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1741-2552\/ada0e5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,1]]},"references-count":43,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,12,30]]},"published-print":{"date-parts":[[2024,12,1]]}},"URL":"https:\/\/doi.org\/10.1088\/1741-2552\/ada0e5","relation":{},"ISSN":["1741-2560","1741-2552"],"issn-type":[{"value":"1741-2560","type":"print"},{"value":"1741-2552","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,1]]},"assertion":[{"value":"Multi-dimensional hybrid bilinear CNN-LSTM models for epileptic seizure detection and prediction using EEG signals","name":"article_title","label":"Article Title"},{"value":"Journal of Neural Engineering","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2024-08-05","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-12-18","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-12-30","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}