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Video-based automatic seizure detection in pharmacoresistant epilepsy: A prospective exploratory study
Department of Neurology and Department of Biomedical and Clinical Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden.
Department of Neurology and Department of Biomedical and Clinical Sciences, Faculty of Medicine and Health Sciences, Linköping University, Linköping, Sweden.
Department of Neurophysiology and Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
Örebro University, School of Medical Sciences. Örebro University Hospital. Department of Neurosurgery, Örebro University Hospital, Sweden; Department of Neurosurgery and Department of Biomedical and Clinical Sciences, Linköping University, Sweden.ORCID iD: 0000-0003-0799-2148
2024 (English)In: Epilepsy & Behavior, ISSN 1525-5050, E-ISSN 1525-5069, Vol. 161, article id 110118Article in journal (Refereed) Published
Abstract [en]

OBJECTIVE: The objective of this study was to evaluate the diagnostic yield and clinical utility of an automated AI video-based seizure detection device, Nelli®, (SDD) in pharmacoresistant epilepsy patients. The SDD captures and automatically classifies nocturnal motor behavior suggestive of epileptic seizures or non-epileptic motor behavior of potential clinical value.

METHODS: Patients with focal epilepsy and pharmacoresistance referred for inpatient long-term video-EEG monitoring were prospectively recruited. Participants were monitored in their home at night with the SDD for a median of 15.5 nights. Captured video recordings were analyzed by clinical experts and each SDD-registration session was classified as diagnostic or not. Clinical utility for each participant was assessed from pre-specified utility measures. The outcome measures were compared between major focal motor and subtle focal motor seizures.

RESULTS: One SDD-registration session in each of the 20 participants was performed and analyzed. Video recordings were captured in 18 sessions. Diagnostic yield was found in 11 registration sessions (55.0 %) and clinical utility in 8 registration sessions (40.0 %). No significant difference was found between the AI-algorithm classification and clinical experts' consensus assessment of captured video recordings as epileptic or not. Positive predictive value was 81.8 % for registration sessions containing video recordings classified as epileptic seizures. The diagnostic yield and clinical utility were significantly higher among major focal motor seizures (81.8 % and 63.6 %) compared to subtle focal motor seizures.

SIGNIFICANCE: The SDD is useful to evaluate patients with pharmacoresistant epilepsy and major focal motor seizures (hyperkinetic, tonic, clonic, focal to bilateral tonic-clonic seizures); it may facilitate the diagnostic process in patients referred for long-term inpatient video-EEG evaluation and beneficially change anti-seizure treatments. The SDD provided accurate classification of major focal motor seizures as epileptic, or non-epileptic, and may serve as a useful diagnostic tool to distinguish epileptic and non-epileptic episodic events with a prominent motor component.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 161, article id 110118
Keywords [en]
Automated seizure detection, Epilepsy, Epilepsy diagnostics, Epilepsy monitoring, Pharmacoresistant epilepsy, Seizure investigation, Video-EEG
National Category
Neurology
Identifiers
URN: urn:nbn:se:oru:diva-117341DOI: 10.1016/j.yebeh.2024.110118ISI: 001357543300001PubMedID: 39536364Scopus ID: 2-s2.0-85208662986OAI: oai:DiVA.org:oru-117341DiVA, id: diva2:1913241
Funder
Region Östergötland, RÖ-962784; RÖ-987177; RÖ-982163
Note

This work was supported by the Department of Neurology, Linköping, Sweden, and Region Östergötland (RÖ-962784; RÖ-987177; RÖ-982163).

Available from: 2024-11-14 Created: 2024-11-14 Last updated: 2024-11-28Bibliographically approved

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