Video-based automatic seizure detection in pharmacoresistant epilepsy: A prospective exploratory study
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).
2024-11-142024-11-142024-11-28Bibliographically approved