{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T20:30:50Z","timestamp":1790454650188,"version":"4.1.0"},"reference-count":55,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100013883","name":"LGS Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013883","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epilepsia"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>The discovery and validation of electroencephalography (EEG) biomarkers often rely on visual identification of waveforms. However, bias toward visually striking events restricts the search space for new biomarkers, and low interrater reliability can limit rigorous validation. We present a data\u2010driven approach to biomarker discovery called scalp EEG Pattern Identification and Categorization (s\u2010EPIC), which enables automated, unsupervised identification of EEG waveforms. S\u2010EPIC is validated on Lennox\u2013Gastaut syndrome (LGS), an epilepsy that is difficult to diagnose and assess due to its variable presentation and insidious evolution of symptoms.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We retrospectively collected 10\u2010min scalp EEG clips during non\u2013rapid eye movement (NREM) sleep from 20 subjects with LGS and 20 approximately age\u2010matched healthy controls. For s\u2010EPIC, EEG events of interest (EOIs) were detected in all subjects using time\u2010frequency analysis. The 11\u2009705 EOIs were characterized based on 11 features and were collectively grouped using both k\u2010means clustering and feature categorization. To provide clinical context, 1350 EOIs were visually reviewed and classified by three epileptologists.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>s\u2010EPIC identified four clusters as candidate biomarkers of LGS, each having significantly more LGS EOIs than control EOIs. Two clusters contained EOIs resembling known LGS biomarkers such as interictal epileptiform discharges and generalized paroxysmal fast activity. The other two LGS\u2010associated EEG clusters contained short bursts of power in beta and gamma frequency bands that were primarily unrecognized by epileptologists. This approach also uncovered significant differences in sleep spindles between LGS and control cohorts.<\/jats:p><\/jats:sec><jats:sec><jats:title>Significance<\/jats:title><jats:p>s\u2010EPIC provides a quantitative approach to waveform identification that could be broadly applied to EEG from both healthy subjects and those with suspected pathology. s\u2010EPIC can objectively identify and characterize relevant EEG waveforms without visual review or assumptions about the waveform's morphology and could therefore be a powerful tool for the discovery and refinement of EEG biomarkers.<\/jats:p><\/jats:sec>","DOI":"10.1111\/epi.18211","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T16:04:11Z","timestamp":1734019451000},"page":"541-553","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Discovering <scp>EEG<\/scp> biomarkers of Lennox\u2013Gastaut syndrome through unsupervised 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