High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence

Abstract

High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity. A number of automated detection algorithms have been developed for reliable analysis of invasively recorded HFOs. However, invasive recordings are not widely applicable since they bear risks and costs, and the harm of the surgical intervention of implantation needs to be weighted against the informational benefits of the invasive examination. In contrast, scalp EEG is widely available at low costs and does not bear any risks. However, the detection of HFOs on the scalp represents a challenge that was taken on so far mostly via visual detection. Visual detection of HFOs is, in turn, highly time-consuming and subjective. In this review, we discuss that automated detection algorithms for detection of HFOs on the scalp are highly warranted because the available algorithms were all developed for invasively recorded EEG and do not perform satisfactorily in scalp EEG because of the low signal-to-noise ratio and numerous artefacts as well as physiological activity that obscures the tiny phenomena in the high-frequency range.

Localization of the Epileptogenic Zone Using High Frequency Oscillations

“…HFO detection has greatly benefitted from the development of automated detectors (see 64, for a 2016 review) (30, 54, 73–76). It is well-known that visual HFO detection is very time-consuming, and the reliability of this procedure has been questioned on several occasions (16).…”

“…First, evidence on HFOs as a clinically relevant biomarker stems predominantly from retrospective assessments with visual marking of HFOs, leading to problems of reproducibility and reliability (24, 25). Second, there are also physiologic, non-epileptic HFOs and their existence poses a challenge, as disentangling them from clinically relevant pathologic HFOs still is an unsolved issue with considerable influence on HFO research (26–30). Such a distinction is crucial to further investigate the clinical value of HFOs in predicting outcome after epilepsy surgery.…”

Fast oscillations >40Hz localize the epileptogenic zone: an electrical source imaging study using high-density electroencephalography

“…Prolonged clean recordings can be extremely valuable for FO identification, as data have the least artifacts in the high-frequency domain during sleep [Zijlmans et al, 2017]. Yet in the case of prolonged recordings it might not be feasible to visually mark and validate each FO and a computational approach might be needed [Höller et al, 2018]. Another interesting aspect that might influence the results is the type of electrode being used.…”

MEEGIPS—A Modular EEG Investigation and Processing System for Visual and Automated Detection of High Frequency Oscillations

“…Nevertheless, we must keep in mind that deep neural networks perform only well on huge amounts of training data, which we do not have in terms of a gold standard ground truth of manual markings of HFOs. Sophisticated engineering of data augmentation or learning based on simulated data (Höller et al, 2018) could be part of MEEGIPS in future releases.…”

“…Detection of scalp HFOs is much more time consuming, error-prone and difficult than detection of HFOs in invasive recordings. Therefore, automated detection is highly warranted (Höller et al, 2018). A number of recent studies set out to detect fast oscillations non-invasively in the magnetoencephalogram (MEG) (e.g., Papadelis et al, 2016; Pellegrino et al, 2016; van Klink et al, 2016; von Ellenrieder et al, 2016; Migliorelli et al, 2017; Tamilia et al, 2017).…”