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Pitfalls in scalp high-frequency oscillation detection from long-term EEG monitoring

Pitfalls in scalp high-frequency oscillation detection from long-term EEG monitoring

Title: Pitfalls in scalp high-frequency oscillation detection from long-term EEG monitoring
Author: Gerner, Nathalie
Thomschewski, Aljoscha
Marcu, Adrian
Trinka, Eugen
Höller, Yvonne   orcid.org/0000-0002-1727-8557
Date: 2020-06-02
Language: English
Scope: 432
University/Institute: Háskólinn á Akureyri
University of Akureyri
School: Hug- og félagsvísindasvið (HA)
School of Humanities and Social Sciences (UA)
Department: Sálfræðideild (HA)
Faculty of Psychology (UA)
Series: Frontiers in Neurology;11
ISSN: 1664-2295
DOI: 10.3389/fneur.2020.00432
Subject: Neurology; Brain; Epilepsy; Taugasjúkdómar; Heilinn; Flogaveiki
URI: https://hdl.handle.net/20.500.11815/1950

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Gerner, N., Thomschewski, A., Marcu, A., Trinka, E. og Höller, Y. (2020). Pitfalls in scalp high-frequency oscillation detection from long-term EEG monitoring. Frontiers in Neurology, 11, 432. doi:10.3389/fneur.2020.00432


Aims: Intracranially recorded high-frequency oscillations (>80 Hz) are considered a candidate epilepsy biomarker. Recent studies claimed their detectability on the scalp surface. We aimed to investigate the applicability of high-frequency oscillation analysis to routine surface EEG obtained at an epilepsy monitoring unit. Methods: We retrospectively analyzed surface EEGs of 18 patients with focal epilepsy and six controls, recorded during sleep under maximal medication withdrawal. As a proof of principle, the occurrence of motor task-related events during wakefulness was analyzed in a subsample of six patients with seizure- or syncope-related motor symptoms. Ripples (80–250 Hz) and fast ripples (>250 Hz) were identified by semi-automatic detection. Using semi-parametric statistics, differences in spontaneous and task-related occurrence rates were examined within subjects and between diagnostic groups considering the factors diagnosis, brain region, ripple type, and task condition. Results: We detected high-frequency oscillations in 17 out of 18 patients and in four out of six controls. Results did not show statistically significant differences in the mean rates of event occurrences, neither regarding the laterality of the epileptic focus, nor with respect to active and inactive task conditions, or the moving hand laterality. Significant differences in general spontaneous incidence [WTS(1) = 9.594; p = 0.005] that indicated higher rates of fast ripples compared to ripples, notably in patients with epilepsy compared to the control group, may be explained by variations in data quality. Conclusion: The current analysis methods are prone to biases. A common agreement on a standard operating procedure is needed to ensure reliable and economic detection of high-frequency oscillations.


Copyright © 2020 Gerner, Thomschewski, Marcu, Trinka and Höller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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