{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T07:34:16Z","timestamp":1790408056630,"version":"4.1.0"},"reference-count":21,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T00:00:00Z","timestamp":1611014400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epilepsia"],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>A reliable identification of a high\u2010risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated the ability of prodromal symptoms to predict preictal states using a machine learning (ML) approach. Twenty\u2010four patients with drug\u2010resistant epilepsy were admitted for continuous video\u2010electroencephalographic monitoring and filled out a daily four\u2010point questionnaire on prodromal symptoms. Data were then classified into (1) a preictal group for questionnaires completed in a 24\u2010h period prior to at least one seizure (<jats:italic>n<\/jats:italic><jats:sub>1<\/jats:sub>\u00a0=\u00a058) and (2) an interictal group for questionnaires completed in a 24\u2010h period without seizures (<jats:italic>n<\/jats:italic><jats:sub>2<\/jats:sub>\u00a0=\u00a0190). Our prediction model was based on a support vector machine classifier and compared to a Fisher's linear classifier. The combination of all the prodromal symptoms yielded a good prediction performance (area under the curve [AUC]\u00a0=\u00a0.72, 95% confidence interval [CI] = .61\u2013.81). This performance was significantly enhanced by selecting a subset of the most relevant symptoms (AUC\u00a0=\u00a0.80, 95% CI = .69\u2013.88). In comparison, the linear classifier systematically failed (AUCs\u00a0&lt;\u00a0.6). Our findings indicate that the ML analysis of prodromal symptoms is a promising approach to identifying preictal states prior to seizures. This could pave the way for development of clinical strategies in seizure prevention and even a noninvasive alarm system.<\/jats:p>","DOI":"10.1111\/epi.16804","type":"journal-article","created":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T19:04:56Z","timestamp":1611083096000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Preictal state detection using prodromal symptoms: A machine learning approach"],"prefix":"10.1111","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1407-5575","authenticated-orcid":false,"given":"Louis","family":"Cousyn","sequence":"first","affiliation":[{"name":"Department of Neurology Epilepsy Unit Piti\u00e9\u2010Salp\u00eatri\u00e8re Hospital Public Hospital Network of Paris Paris France"},{"name":"Paris Brain Institute ICM (INSERM\u2010U1127, CNRS\u2010UMR7225) Paris France"},{"name":"Center of Reference for Rare Epilepsies Piti\u00e9\u2010Salp\u00eatri\u00e8re Hospital Paris France"},{"name":"Sorbonne University Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0077-8114","authenticated-orcid":false,"given":"Vincent","family":"Navarro","sequence":"additional","affiliation":[{"name":"Department of Neurology Epilepsy Unit Piti\u00e9\u2010Salp\u00eatri\u00e8re Hospital Public Hospital Network of Paris Paris France"},{"name":"Paris Brain Institute ICM (INSERM\u2010U1127, CNRS\u2010UMR7225) Paris France"},{"name":"Center of Reference for Rare Epilepsies Piti\u00e9\u2010Salp\u00eatri\u00e8re Hospital Paris France"},{"name":"Sorbonne University Paris France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Chavez","sequence":"additional","affiliation":[{"name":"Paris Brain Institute ICM (INSERM\u2010U1127, CNRS\u2010UMR7225) Paris 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