Under‐reporting of nocturnal seizures using video‐based home monitoring: a case study on the evaluation of the effect of vagal nerve stimulation

Peciola, Stefano; Himanen, Sari-Leena; Knight, Andrew; Dibué-Adjei, Maxine; Rainesalo, Sirpa; Peltola, Jukka

doi:10.1684/epd.2018.1018

Cited by 8 publications

(6 citation statements)

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“…These patients were also monitored with a CEmarked novel video/audio-based seizure detection system (Nelli). 8,9 Nelli detects activity that is indicative of seizure events with a positive motor component using audio/video recordings. Pretrained detection models with selected thresholds provide captured events including securely accessible audio/video recordings of those events to clinical professionals.…”

Section: Methodsmentioning

confidence: 99%

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Semiautomated classification of nocturnal seizures using video recordings

Peltola

Basnyat

Larsen³

et al. 2022

Epilepsia

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ObjectiveThe objective of this study was to evaluate the accuracy of a semiautomated classification of nocturnal seizures using a hybrid system consisting of an artificial intelligence‐based algorithm, which selects epochs with potential clinical relevance to be reviewed by human experts.MethodsConsecutive patients with nocturnal motor seizures admitted for video‐electroencephalographic long‐term monitoring (LTM) were prospectively recruited. We determined the extent of data reduction by using the algorithm, and we evaluated the accuracy of seizure classification from the hybrid system compared with the gold standard of LTM.ResultsForty consecutive patients (24 male; median age = 15 years) were analyzed. The algorithm reduced the duration of epochs to be reviewed to 14% of the total recording time (1874 h). There was a fair agreement beyond chance in seizure classification between the hybrid system and the gold standard (agreement coefficient = .33, 95% confidence interval = .20–.47). The hybrid system correctly identified all tonic–clonic and clonic seizures and 82% of focal motor seizures. However, there was low accuracy in identifying seizure types with more discrete or subtle motor phenomena.SignificanceUsing a hybrid (algorithm–human) system for reviewing nocturnal video recordings significantly decreased the workload and provided accurate classification of major motor seizures (tonic–clonic, clonic, and focal motor seizures).

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Section: Methodsmentioning

confidence: 99%

“…These patients were also monitored with a CE‐marked novel video/audio‐based seizure detection system (Nelli) 8,9 . Nelli detects activity that is indicative of seizure events with a positive motor component using audio/video recordings.…”

Section: Methodsmentioning

confidence: 99%

Semiautomated classification of nocturnal seizures using video recordings

Peltola

Basnyat

Larsen³

et al. 2022

Epilepsia

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“…Furthermore, all seizures were self-reported, which implies the probability of underreporting (e.g., of nocturnal seizures). 15 Only items that emerged from the in-depth initial interviews were collected during the course of this study. Stressors or resources that patients were unaware of and did not mention could therefore not be considered.…”

Section: Limitations and Strengths Of The Studymentioning

confidence: 99%

Psychological precursors of epileptic seizures

Michaelis,

Schöller,

Popkirov

et al. 2024

Epilepsia

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Psychological stress is the most commonly self‐reported precursor of epileptic seizures. However, retrospective and prospective studies remain inconclusive in this regard. Here, we explored whether seizures would be preceded by significant changes in reported stressors or resource utilization.This study is based on high‐frequency time series through daily online completion of personalized questionnaires of 9‐24 items in epilepsy outpatients and compared responses 1‐14 days before seizures with interictal time series.Fourteen patients (79% women, age from 23 to 64 years) completed daily questionnaires over a period of 87 – 898 days (median: 277 days = 9.2 months). A total of 4560 fully completed daily questionnaires were analyzed, 685 of which included reported seizure events. Statistically significant changes in preictal compared to interictal dynamics were found in 11/14 patients (79%) across 41 items (22% of all 187 items). In 7/14 patients (50%) seizures were preceded by a significant mean increase of stressors and/or a significant mean decrease of resource utilization.This exploratory analysis of long‐term prospective individual patient data of specific stressors and personal coping strategies generates the hypothesis that medium‐term changes in psychological well‐being may precede the occurrence of epileptic seizures in some patients.

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“…The Nelli Ò seizure monitoring system is an audio/video-based semi-automatic (hybrid) seizure monitoring platform that uses computer vision and machine learning to identify kinematic data commonly associated with seizures with a positive motor component and human experts to visually assess these epochs [16,17]. In a recent validation study, the Nelli Ò hybrid system was used in a blinded setting without any prior information on the patients or their seizure types against video-EEG monitoring at a well-established epilepsy center providing accurate classification of major motor seizures including tonic-clonic, clonic, and focal motor seizures [18].…”

Section: Contents Lists Available At Sciencedirectmentioning

confidence: 99%