{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T11:23:30Z","timestamp":1787397810936,"version":"build-2736575974"},"reference-count":39,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epileptic Disorders"],"published-print":{"date-parts":[[2026,8]]},"abstract":"Abstract<\/jats:title>\n \n Objective<\/jats:title>\n \n To identify clinically meaningful patterns in ictal electroclinical features of focal epilepsy using a data\u2010driven, unsupervised learning approach, and to assess whether such patterns can localize and lateralize the epileptogenic zone (\n EZ<\/jats:styled-content>\n ) more accurately than conventional electroclinical interpretation.\n <\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n Ictal scalp\n EEG<\/jats:styled-content>\n and seizure semiology were systematically extracted using the\n SCORE<\/jats:styled-content>\n system in 116 patients with drug\u2010resistant focal epilepsy who subsequently underwent resective surgery. Unsupervised clustering was applied separately to\n EEG<\/jats:styled-content>\n and semiology features using k\u2010means and agglomerative hierarchical clustering. Cluster quality was evaluated using the silhouette score, within\u2010cluster sum of squares (\n WSS<\/jats:styled-content>\n ), between\u2010cluster sum of squares (\n BSS<\/jats:styled-content>\n ), the ratio of between\u2010cluster to within\u2010cluster variance (\n BSS<\/jats:styled-content>\n \/\n WSS<\/jats:styled-content>\n ), the proportion of variance explained by clustering (\n BSS<\/jats:styled-content>\n \/\n TSS<\/jats:styled-content>\n ), and the Calinski\u2013Harabasz index. The ability of the resulting clusters to localize and lateralize the temporal lobe\n EZ<\/jats:styled-content>\n was assessed and compared with the conventional approach reflecting current clinical practice, based on previously published lateralizing signs, semiology localizing to the temporal lobe, and scalp\n EEG<\/jats:styled-content>\n localization of ictal onset.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Unsupervised clustering achieved strong structure for\n EEG<\/jats:styled-content>\n and moderate for semiology data. K\u2010means clustering of\n EEG<\/jats:styled-content>\n features yielded a silhouette score of .63, while hierarchical clustering of semiology features achieved a silhouette score of .54. Semiology\u2010based clusters showed substantial overlap with respect to both localization and lateralization.\n EEG<\/jats:styled-content>\n \u2010based clusters localized and lateralized the temporal lobe\n EZ<\/jats:styled-content>\n with an accuracy of 65.3% (95%\n CI<\/jats:styled-content>\n : 55.0%\u201374.6%). This performance was comparable to conventional electroclinical interpretation using scalp\n EEG<\/jats:styled-content>\n ictal onset (68.1%, 95%\n CI<\/jats:styled-content>\n : 57.5%\u201377.5%). Interpretation based on seizure semiology achieved an accuracy of 71.3% (95% CI: 61.0\u201380.1%).\n <\/jats:p>\n <\/jats:sec>\n \n Significance<\/jats:title>\n \n Unsupervised clustering of ictal electroclinical features did not outperform conventional expert interpretation in localizing or lateralizing the\n EZ<\/jats:styled-content>\n . These findings suggest that the limitations may be inherent to the observable ictal phenomena captured by scalp\n EEG<\/jats:styled-content>\n and semiology. Future research should focus on identifying novel or complementary biomarkers to improve precision in epilepsy surgery evaluation.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/epd2.70283","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T05:52:01Z","timestamp":1779256321000},"page":"1236-1251","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised clustering of electroclinical features in temporal lobe epilepsy: A data\u2010driven approach"],"prefix":"10.1002","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5786-6954","authenticated-orcid":false,"given":"Maria","family":"Vlachou","sequence":"first","affiliation":[{"name":"Department of Clinical Neurophysiology Danish Epilepsy Center Dianalund 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Filadelfia University of Copenhagen Copenhagen Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9024-7936","authenticated-orcid":false,"given":"Lars Hageman","family":"Pinborg","sequence":"additional","affiliation":[{"name":"Neurobiology Research Unit and Epilepsy Clinic, Department of Neurology Copenhagen University Hospital Rigshospitalet Copenhagen Denmark"},{"name":"Department of Clinical Medicine University of Copenhagen Copenhagen Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6035-6581","authenticated-orcid":false,"given":"S\u00e1ndor","family":"Beniczky","sequence":"additional","affiliation":[{"name":"Department of Clinical Medicine University of Copenhagen Copenhagen Denmark"},{"name":"Department of Clinical Neurophysiology Danish Epilepsy Centre Dianalund Denmark"},{"name":"Copenhagen University Hospital Rigshospitalet Copenhagen 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