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There is a need for automated seizure characterization, to identify high\u2010risk seizures. Wearable devices can automatically identify seizure types with the highest associated morbidity and mortality (generalized tonic\u2010clonic seizures), quantify their duration and frequency, and provide data on postictal position and immobility, autonomic changes derived from electrocardiography\/heart rate variability, electrodermal activity, respiration, and oxygen saturation. In this review, we summarize how these biosignals reflect seizure severity, and how they can be monitored in the ambulatory outpatient setting using wearable devices. Multimodal recording of these biosignals will provide valuable information for individual risk assessment, as well as insights into the mechanisms and prevention of sudden unexpected death in epilepsy.<\/jats:p>","DOI":"10.1111\/epi.16492","type":"journal-article","created":{"date-parts":[[2020,6,10]],"date-time":"2020-06-10T10:54:20Z","timestamp":1591786460000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Biomarkers of seizure severity derived from wearable devices"],"prefix":"10.1111","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6035-6581","authenticated-orcid":false,"given":"S\u00e1ndor","family":"Beniczky","sequence":"first","affiliation":[{"name":"Department of Clinical Neurophysiology Danish Epilepsy Center Dianalund Denmark"},{"name":"Department of Clinical Neurophysiology Aarhus University Hospital Aarhus Denmark"},{"name":"Department of Clinical Medicine Aarhus University Aarhus 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