Significance of High-frequency Electrical Brain Activity

　Electroencephalogram (EEG) data include broadband electrical brain activity ranging from infra-slow bands (200 / 250 Hz, respectively) are particularly of note due to their very close relationship to epileptogenicity, with the possibility that they could function as a surrogate biomarker of epileptogenicity. In contrast, physiological high-frequency activity plays an important role in higher brain functions, and the differentiation between pathological / epileptic and physiological HFOs is a critical issue, especially in epilepsy surgery. HFOs were initially recorded with intracranial electrodes in patients with intractable epilepsy as part of a long-term invasive seizure monitoring study. However, fast oscillations (FOs) in the ripple and gamma bands (40-80 Hz) are now noninvasively detected by scalp EEG and magnetoencephalography, and thus the scope of studies on HFOs /FOs is rapidly expanding

Human Intracranial High Frequency Oscillation Detection Using Time Frequency Analysis and Its Relation to the Seizure Onset Zone

One third of the patients diagnosed with focal epilepsy do not respond to antiepileptic drugs. For these patients the possible diagnosis options to give seizure freedom or at least reduce seizure frequencies significantly would be surgical resection or seizure interrupting implantable devices. The success of these procedures depends on accurate detection of the region causing seizure also known as epileptic zone. This requires detail pre-surgical evaluation including Invasive Video Electroencephalographic Monitoring (IVEM). The resulting great volume of intracranial Electroencephalography (iEEG) signal is visually examined by an expert epileptologist which can be time consuming, extremely complex, and not always effective. We have introduced an automated method to help the epileptologist analyze the iEEG signals. Literature suggest that signals recorded from brain regions subject to seizure activity produce a short durational high gamma ripple activity in the iEEG called High Frequency Oscillations (HFOs). The algorithm presented in this thesis uses an automated time-frequency space analysis method to detect HFOs and distinguish them from high frequency artifacts. As HFOs are short-lived high frequency oscillations, the time-frequency space analysis method chosen should have good time and frequency resolution capabilities. The Stockwell transform was used for this purpose which is a variable window version of the Short Time Fourier Transform (STFT). We have modified the detection algorithm to analyze the multi-channel iEEG data obtained from patients monitored at the Spectrum Health Epilepsy Monitoring Unit (EMU) and found that the electrode site recordings exhibiting higher HFO rate are within the Seizure Onset Zone (SOZ) determined by visual examination of the iEEG recordings by the epileptologist. These electrodes also continue to show higher HFO rate throughout the entire study. The HFO analysis presented in this thesis suggests that HFO detection and identification may be used to reduce IVEM monitoring time by aiding the neurosurgeon delineating the epileptic zone in relatively shorter time. This will lead to better surgical outcome or succesful implantation of the seizure intervention devices

High frequency oscillations in relation to interictal spikes in predicting postsurgical seizure freedom

We evaluate whether interictal spikes, epileptiform HFOs and their co-occurrence (Spike + HFO) were included in the resection area with respect to seizure outcome. We also characterise the relationship between high frequency oscillations (HFOs) and propagating spikes. We analysed intracranial EEG of 20 patients that underwent resective epilepsy surgery. The co-occurrence of ripples and fast ripples was considered an HFO event; the co-occurrence of an interictal spike and HFO was considered a Spike + HFO event. HFO distribution and spike onset were compared in cases of spike propagation. Accuracy in predicting seizure outcome was 85% for HFO, 60% for Spikes, and 79% for Spike + HFO. Sensitivity was 57% for HFO, 71% for Spikes and 67% for Spikes + HFO. Specificity was 100% for HFO, 54% for Spikes and 85% for Spikes + HFO. In 2/2 patients with spike propagation, the spike onset included the HFO area. Combining interictal spikes with HFO had comparable accuracy to HFO. In patients with propagating spikes, HFO rate was maximal at the onset of spike propagation

Automated detection of epileptic ripples in MEG using beamformer-based virtual sensors

Objective. In epilepsy, high-frequency oscillations (HFOs) are expressively linked to the seizure onset zone (SOZ). The detection of HFOs in the noninvasive signals from scalp electroencephalography (EEG) and magnetoencephalography (MEG) is still a challenging task. The aim of this study was to automate the detection of ripples in MEG signals by reducing the high-frequency noise using beamformer-based virtual sensors (VSs) and applying an automatic procedure for exploring the time-frequency content of the detected events. Approach. Two-hundred seconds of MEG signal and simultaneous iEEG were selected from nine patients with refractory epilepsy. A two-stage algorithm was implemented. Firstly, beamforming was applied to the whole head to delimitate the region of interest (ROI) within a coarse grid of MEG-VS. Secondly, a beamformer using a finer grid in the ROI was computed. The automatic detection of ripples was performed using the time-frequency response provided by the Stockwell transform. Performance was evaluated through comparisons with simultaneous iEEG signals. Main results. ROIs were located within the seizure-generating lobes in the nine subjects. Precision and sensitivity values were 79.18% and 68.88%, respectively, by considering iEEG-detected events as benchmarks. A higher number of ripples were detected inside the ROI compared to the same region in the contralateral lobe. Significance. The evaluation of interictal ripples using non-invasive techniques can help in the delimitation of the epileptogenic zone and guide placement of intracranial electrodes. This is the first study that automatically detects ripples in MEG in the time domain located within the clinically expected epileptic area taking into account the time-frequency characteristics of the events through the whole signal spectrum. The algorithm was tested against intracranial recordings, the current gold standard. Further studies should explore this approach to enable the localization of noninvasively recorded HFOs to help during pre-surgical planning and to reduce the need for invasive diagnostics.Peer ReviewedPostprint (author's final draft

High-frequency oscillations in scalp EEG: A systematic review of methodological choices and clinical findings

Objective: Pathological high-frequency oscillations (HFOs) in intracranial EEG are promising biomarkers of epileptogenic tissue, and their physiological counterparts play a role in sensorimotor and cognitive function. HFOs have also been found in scalp EEG, but an overview of all studies is lacking. In this systematic review, we assessed the methodology to detect scalp HFOs and their clinical potential. Methods: We searched PubMed, Embase and the Cochrane Library for studies on HFOs in scalp EEG, and extracted methodological and clinical data. Results: We included 60 studies with data from 1149 unique individuals. Two-thirds of studies analyzed HFOs visually in the time or time–frequency domain, and one-third automatically with visual validation. Most studies evaluated interictal ripples during sleep in children. Pathological HFOs were overall better than spikes in localizing the epileptogenic zone and predicting outcome, correlated negatively with cognition and positively with disease activity and severity, and decreased after medical and surgical treatment. Conclusions: The methodologies of the 60 studies were heterogeneous, but pathological scalp HFOs were clinically valuable as biomarkers in various situations, particularly in children with epilepsy. Significance: This systematic review gives an extensive overview of methodological and clinical data on scalp HFOs, establishing their clinical potential and discussing their limitations