Differentiating ictal/subclinical spikes and waves in childhood absence epilepsy by spectral and network analyses: A pilot study. Issue 9 (September 2021)

• Record Type: Journal Article
• Authors: Kumar, Ami; Lyzhko, Ekaterina; Hamid, Laith; Srivastav, Anand; Stephani, Ulrich; Japaridze, Natia
• Is Part Of: Clinical neurophysiology. Volume 132:Issue 9(2021)
• Page Start: 2222
• Page End: 2231
• Publication Date: 2021-09
• Subjects: Childhood Absence Epilepsy -- EEG -- Spectral analysis -- Functional Connectivity -- ictal GSWDs -- subclinical GSWDs; CAE Childhood absence epilepsy -- GSWDs generalized spikes and waves discharges -- EEG electroencephalography -- FC functional connectivity -- MEG magnetoencephalography -- EEG-fMRI EEG combined with functional magnetic resonance imaging -- DMN default mode network -- FFT Fast Fourier Transform -- ANOVA analysis of variance; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2021.06.011
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Spectral and network analyses can be used to differentiate ictal and subclinical EEG discharges in childhood absence epilepsy, in a routine setting. Ictal transition periods (pre-during-post) have significantly higher spectral power compared to subclinical discharges. Ictal transition periods show significantly weaker connectivity between EEG channels as compared to subclinical discharges. Abstract: Objective: Childhood absence epilepsy (CAE) is a disease with distinct seizure semiology and electroencephalographic (EEG) features. Differentiating ictal and subclinical generalized spikes and waves discharges (GSWDs) in the EEG is challenging, since they appear to be identical upon visual inspection. Here, spectral and functional connectivity (FC) analyses were applied to routine EEG data of CAE patients, to differentiate ictal and subclinical GSWDs. Methods: Twelve CAE patients with both ictal and subclinical GSWDs were retrospectively selected for this study. The selected EEG epochs were subjected to frequency analysis in the range of 1–30 Hz. Further, FC analysis based on the imaginary part of coherency was used to determine sensor level networks. Results: Delta, alpha and beta band frequencies during ictal GSWDs showed significantly higher power compared to subclinical GSWDs. FC showed significant network differences for all frequency bands, demonstrating weaker connectivity between channels during ictal GSWDs. Conclusion: Using spectral and FC analyses significant differences between ictal and subclinical GSWDs in CAE patients were detected, suggesting that these features could be used for machine learning classification purposes to improve EEG monitoring. Significance: Identifying differences between ictal and subclinical GSWDs using routine EEG, may improve understanding of this syndrome and the management of patients with CAE.