Preictal state detection using prodromal symptoms: A machine learning approach.
Adult
Affect
/ physiology
Area Under Curve
Attention
/ physiology
Comprehension
/ physiology
Drug Resistant Epilepsy
/ physiopathology
Electroencephalography
Female
Hearing Loss
/ physiopathology
Humans
Machine Learning
Male
Middle Aged
Noise
Photophobia
/ physiopathology
Prodromal Symptoms
Reading
Seizures
/ physiopathology
Speech
/ physiology
Support Vector Machine
Surveys and Questionnaires
Tinnitus
/ physiopathology
Video Recording
Vision Disorders
/ physiopathology
Young Adult
epilepsy
machine learning
preictal state
prodromal symptoms
prodromes
seizure prediction
Journal
Epilepsia
ISSN: 1528-1167
Titre abrégé: Epilepsia
Pays: United States
ID NLM: 2983306R
Informations de publication
Date de publication:
02 2021
02 2021
Historique:
received:
12
09
2020
revised:
13
12
2020
accepted:
13
12
2020
pubmed:
20
1
2021
medline:
20
4
2021
entrez:
19
1
2021
Statut:
ppublish
Résumé
A reliable identification of a high-risk state for upcoming seizures may allow for preemptive treatment and improve the quality of patients' lives. We evaluated the ability of prodromal symptoms to predict preictal states using a machine learning (ML) approach. Twenty-four patients with drug-resistant epilepsy were admitted for continuous video-electroencephalographic monitoring and filled out a daily four-point questionnaire on prodromal symptoms. Data were then classified into (1) a preictal group for questionnaires completed in a 24-h period prior to at least one seizure (n
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e42-e47Informations de copyright
© 2021 International League Against Epilepsy.
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