Clinical classification of psychogenic non-epileptic seizures based on video-EEG analysis and automatic clustering.
Details
Serval ID
serval:BIB_0C118EEF43D7
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Clinical classification of psychogenic non-epileptic seizures based on video-EEG analysis and automatic clustering.
Journal
Journal of neurology, neurosurgery, and psychiatry
ISSN
1468-330X (Electronic)
ISSN-L
0022-3050
Publication state
Published
Issued date
09/2011
Peer-reviewed
Oui
Volume
82
Number
9
Pages
955-960
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Publication Status: ppublish
Abstract
Psychogenic non-epileptic seizures (PNES) or attacks consist of paroxysmal behavioural changes that resemble an epileptic seizure but are not associated with electrophysiological epileptic changes. They are caused by a psychopathological process and are primarily diagnosed on history and video-EEG. Clinical presentation comprises a wide range of symptoms and signs, which are individually neither totally specific nor sensitive, making positive diagnosis of PNES difficult. Consequently, PNES are often misdiagnosed as epilepsy. The aim of this study was to identify homogeneous groups of PNES based on specific combinations of clinical signs with a view to improving timely diagnosis.
The authors first retrospectively analysed 22 clinical signs of 145 PNES recorded by video-EEG in 52 patients and then conducted a multiple correspondence analysis and hierarchical cluster analysis.
Five clusters of signs were identified and named according to their main clinical features: dystonic attack with primitive gestural activity (31.6%); pauci-kinetic attack with preserved responsiveness (23.4%); pseudosyncope (16.9%); hyperkinetic prolonged attack with hyperventilation and auras (11.7%); axial dystonic prolonged attack (16.4%). When several attacks were recorded in the same patient, they were automatically classified in the same subtype in 61.5% of patients.
This study proposes an objective clinical classification of PNES based on automatic clustering of clinical signs observed on video-EEG. It also suggests that PNES are stereotyped in the same patient. Application of these findings could help provide an objective diagnosis of patients with PNES.
The authors first retrospectively analysed 22 clinical signs of 145 PNES recorded by video-EEG in 52 patients and then conducted a multiple correspondence analysis and hierarchical cluster analysis.
Five clusters of signs were identified and named according to their main clinical features: dystonic attack with primitive gestural activity (31.6%); pauci-kinetic attack with preserved responsiveness (23.4%); pseudosyncope (16.9%); hyperkinetic prolonged attack with hyperventilation and auras (11.7%); axial dystonic prolonged attack (16.4%). When several attacks were recorded in the same patient, they were automatically classified in the same subtype in 61.5% of patients.
This study proposes an objective clinical classification of PNES based on automatic clustering of clinical signs observed on video-EEG. It also suggests that PNES are stereotyped in the same patient. Application of these findings could help provide an objective diagnosis of patients with PNES.
Keywords
Adolescent, Adult, Age of Onset, Aged, Child, Cluster Analysis, Diagnosis, Differential, Dystonia/etiology, Electroencephalography, Epilepsy/etiology, Female, Humans, Hyperkinesis/physiopathology, Male, Middle Aged, Movement, Retrospective Studies, Seizures/classification, Seizures/etiology, Seizures/psychology, Socioeconomic Factors, Syncope/physiopathology, Young Adult
Pubmed
Web of science
Create date
17/04/2025 11:21
Last modification date
18/04/2025 7:05