Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review.
Comput Struct Biotechnol J
; 24: 66-86, 2024 Dec.
Article
em En
| MEDLINE
| ID: mdl-38204455
ABSTRACT
Background:
Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG.Methods:
We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool.Results:
We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures.Conclusion:
The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG.Significance:
We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Guideline
/
Systematic_reviews
Idioma:
En
Revista:
Comput Struct Biotechnol J
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Canadá