{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T18:48:26Z","timestamp":1789498106904,"version":"build-2803163510"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T00:00:00Z","timestamp":1640217600000},"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><jats:bold>Background:<\/jats:bold> Epileptic seizures are caused by abnormal brain wave hypersynchronization leading to a range of signs and symptoms. Tools for detecting seizures in everyday life typically focus on cardiac rhythm, electrodermal activity, or movement (EMG, accelerometry); however, these modalities are not very effective for non-motor seizures. Ultra long-term subcutaneous EEG-devices can detect the electrographic changes that do not depend on clinical changes. Nonetheless, this also means that it is not possible to assess whether a seizure is clinical or subclinical based on an EEG signal alone. Therefore, we combine EEG and movement-related modalities in this work. We focus on whether it is possible to define an individual \u201cmultimodal ictal fingerprint\u201d which can be exploited in different epilepsy management purposes.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> This study used ultra long-term data from an outpatient monitoring trial of persons with temporal lobe epilepsy obtained with a subcutaneous EEG recording system. Subcutaneous EEG, an EMG estimate and chest-mounted accelerometry were extracted from four persons showing more than 10 well-defined electrographic seizures each. Numerous features were computed from all three modalities. Based on these, the Gini impurity measure of a Random Forest classifier was used to select the most discriminative features for the ictal fingerprint. A total of 74 electrographic seizures were analyzed.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The optimal individual ictal fingerprints included features extracted from all three tested modalities: an acceleration component; the power of the estimated EMG activity; and the relative power in the delta (0.5\u20134 Hz), low theta (4\u20136 Hz), and high theta (6\u20138 Hz) bands of the subcutaneous EEG. Multimodal ictal fingerprints were established for all persons, clustering seizures within persons, while separating seizures across persons.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> The existence of multimodal ictal fingerprints illustrates the benefits of combining multiple modalities such as EEG, EMG, and accelerometry in future epilepsy management. Multimodal ictal fingerprints could be used by doctors to get a better understanding of the individual seizure semiology of people with epilepsy. Furthermore, the multimodal ictal fingerprint gives a better understanding of how seizures manifest simultaneously in different modalities. A knowledge that could be used to improve seizure acknowledgment when reviewing EEG without video.<\/jats:p>","DOI":"10.3389\/fneur.2021.718329","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T11:47:50Z","timestamp":1640260070000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["The Individual Ictal Fingerprint: Combining Movement Measures With Ultra Long-Term Subcutaneous EEG in People With Epilepsy"],"prefix":"10.3389","volume":"12","author":[{"given":"Troels W.","family":"Kjaer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Line S.","family":"Remvig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asbjoern W.","family":"Helge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonas","family":"Duun-Henriksen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,12,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1111\/epi.13671","article-title":"Instruction manual for the ILAE 2017 operational classification of seizure types","volume":"58","author":"Fisher","year":"2017","journal-title":"Epilepsia."},{"key":"B2","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.yebeh.2016.10.036","article-title":"Subjective seizure counts by epilepsy clinical drug trial participants are not reliable","volume":"67","author":"Blachut","year":"2017","journal-title":"Epilepsy Behav."},{"key":"B3","doi-asserted-by":"publisher","first-page":"2204","DOI":"10.1111\/epi.16360","article-title":"Ultra-long-term subcutaneous home monitoring of epilepsy-490 days of EEG from nine patients","volume":"60","author":"Weisdorf","year":"2019","journal-title":"Epilepsia."},{"key":"B4","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1111\/epi.14054","article-title":"Wearable devices for sudden unexpected death in epilepsy prevention","volume":"59","author":"Ryvlin","year":"2018","journal-title":"Epilepsia"},{"key":"B5","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.yebeh.2016.02.039","article-title":"Epileptic seizure onset detection based on EEG and ECG data fusion","volume":"58","author":"Qaraqe","year":"2016","journal-title":"Epilepsy Behav."},{"key":"B6","doi-asserted-by":"publisher","first-page":"S11","DOI":"10.1111\/epi.16521","article-title":"Seizure detection at home: do devices on the market match the needs of people living with epilepsy and their caregivers?","volume":"61","author":"Bruno","year":"2020","journal-title":"Epilepsia."},{"key":"B7","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1111\/epi.16630","article-title":"A new era in electroencephalographic monitoring? 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