{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T16:02:41Z","timestamp":1783440161320,"version":"3.54.6"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T00:00:00Z","timestamp":1644969600000},"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. Neurol."],"abstract":"<jats:p>Seizure detection algorithms are often optimized to detect seizures from the epileptogenic cortex. However, in non-localizable epilepsies, the thalamus is frequently targeted for neuromodulation. Developing a reliable seizure detection algorithm from thalamic SEEG may facilitate the translation of closed-loop neuromodulation. Deep learning algorithms promise reliable seizure detectors, but the major impediment is the lack of larger samples of curated ictal thalamic SEEG needed for training classifiers. We aimed to investigate if synthetic data generated by temporal Generative Adversarial Networks (TGAN) can inflate the sample size to improve the performance of a deep learning classifier of ictal and interictal states from limited samples of thalamic SEEG. Thalamic SEEG from 13 patients (84 seizures) was obtained during stereo EEG evaluation for epilepsy surgery. Overall, TGAN generated synthetic data augmented the performance of the bidirectional Long-Short Term Memory (BiLSTM) performance in classifying thalamic ictal and baseline states. Adding synthetic data improved the accuracy of the detection model by 18.5%. Importantly, this approach can be applied to classify electrographic seizure onset patterns or develop patient-specific seizure detectors from implanted neuromodulation devices.<\/jats:p>","DOI":"10.3389\/fneur.2022.755094","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T14:35:49Z","timestamp":1645022149000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Time-Series Generative Adversarial Network Approach of Deep Learning Improves Seizure Detection From the Human Thalamic SEEG"],"prefix":"10.3389","volume":"13","author":[{"given":"Bhargava","family":"Ganti","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ganne","family":"Chaitanya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ridhanya Sree","family":"Balamurugan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nithin","family":"Nagaraj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karthi","family":"Balasubramanian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandipan","family":"Pati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,2,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/B978-0-444-52898-8.00006-9","article-title":"The epidemiology of the epilepsies","volume":"107","author":"Neligan","year":"2012","journal-title":"Handb Clin Neurol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1016\/S1474-4422(16)30158-2","article-title":"Sudden unexpected death in epilepsy: epidemiology, mechanisms, and prevention","volume":"15","author":"Devinsky","year":"2016","journal-title":"Lancet Neurol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1111\/epi.13699","article-title":"MRI-negative temporal lobe epilepsy-What do we know?","volume":"58","author":"Muhlhofer","year":"2017","journal-title":"Epilepsia."},{"key":"B4","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.seizure.2016.04.002","article-title":"Prognostic factors for seizure outcome in patients with MRI-negative temporal lobe epilepsy: a meta-analysis and systematic review","volume":"38","author":"Wang","year":"2016","journal-title":"Seizure."},{"key":"B5","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1212\/WNL.0000000000001334","article-title":"Long-term efficacy and safety of thalamic stimulation for drug-resistant partial epilepsy","volume":"84","author":"Salanova","year":"2015","journal-title":"Neurology."},{"key":"B6","doi-asserted-by":"publisher","first-page":"43","DOI":"10.3389\/fnsys.2018.00043","article-title":"A comparison of machine learning classifiers for energy-efficient implementation of seizure detection","volume":"12","author":"Manzouri","year":"2018","journal-title":"Front Syst Neurosci"},{"key":"B7","doi-asserted-by":"publisher","first-page":"012089","DOI":"10.1088\/1742-6596\/1453\/1\/012089","article-title":"Machine learning based epileptic seizure detection for responsive neurostimulator system optimization","volume":"1453","author":"Shen","year":"2020","journal-title":"J Phys."},{"key":"B8","doi-asserted-by":"publisher","first-page":"041001","DOI":"10.1088\/1741-2552\/ab094a","article-title":"Early seizure detection for closed loop direct neurostimulation devices in epilepsy","volume":"16","author":"D\u00fcmpelmann","year":"2019","journal-title":"J Neural Eng."},{"key":"B9","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1097\/WNP.0000000000000533","article-title":"Improved patient-independent system for detection of electrical onset of seizures","volume":"36","author":"Sridevi","year":"2019","journal-title":"J Clin Neurophysiol."},{"key":"B10","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1109\/ICACCI.2018.8554697","article-title":"Auto-encoder based automated epilepsy diagnosis","volume-title":"2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI).","author":"Sharathappriyaa","year":"2018"},{"key":"B11","first-page":"101","article-title":"Methods for epileptic seizure prediction using EEG signals: a survey. 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