Literature DB >> 34091130

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review.

Irene Faiman1, Stuart Smith2, John Hodsoll3, Allan H Young4, Paul Shotbolt5.   

Abstract

Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.
Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Diagnostic marker; Epilepsy; Psychogenic nonepileptic seizures; Resting-state EEG; Systematic review; Theta rhythm

Year:  2021        PMID: 34091130     DOI: 10.1016/j.yebeh.2021.108047

Source DB:  PubMed          Journal:  Epilepsy Behav        ISSN: 1525-5050            Impact factor:   2.937


  4 in total

1.  Signal complexity indicators of health status in clinical EEG.

Authors:  Kelly Shen; Alison McFadden; Anthony R McIntosh
Journal:  Sci Rep       Date:  2021-10-12       Impact factor: 4.379

2.  Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline.

Authors:  Majd Abazid; Nesma Houmani; Bernadette Dorizzi; Jerome Boudy; Jean Mariani; Kiyoka Kinugawa
Journal:  Bioengineering (Basel)       Date:  2022-02-04

3.  Prediction Value of Epilepsy Secondary to Inferior Cavity Hemorrhage Based on Scalp EEG Wave Pattern in Deep Learning.

Authors:  Shishuang Jiang; Xuenong He
Journal:  J Healthc Eng       Date:  2022-03-15       Impact factor: 2.682

4.  A Classification Model of EEG Signals Based on RNN-LSTM for Diagnosing Focal and Generalized Epilepsy.

Authors:  Tahereh Najafi; Rosmina Jaafar; Rabani Remli; Wan Asyraf Wan Zaidi
Journal:  Sensors (Basel)       Date:  2022-09-25       Impact factor: 3.847

  4 in total

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