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.

Figure 1

Definition of region of interest according to Burnos et al. [64]. The signal is high-pass filtered (finite impulse response, Blackman windowed sinc) with a cutoff frequency of 80 Hz. A Hilbert transform of the filtered signal (blue) yields a complex output with a 90-degree phase-shifted imaginary part (red). The absolute value of the Hilbert transform is used to generate the signal's envelope (black). The standard deviation of the signal's envelope is the baseline for deriving the threshold for delimiting regions of interest as a first step. As depicted in the figure, closely neighbouring regions are concatenated to form a single one.