Abstract
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.
Highlights
Epilepsy is a neurological disorder that affects about 1% of the world population [1]
The subjects selected for this study presented interictal activity coming from one focal generator
To evaluate the selected region of interest (ROI) resulting from the first stage in the volume reconstruction model, the coordinates of the central virtual sensor were normalized into MNI space using Brainstorm software and the gyrus and lobes were obtained for this position in the normalized atlas
Summary
Epilepsy is a neurological disorder that affects about 1% of the world population [1]. 20% to 40% of patients diagnosed with epilepsy become refractory to treatment [2] For these patients, the most frequent therapeutic alternative is neurosurgical resection of the epileptogenic zone (EZ), defined as the cortex area that is necessary and sufficient for initiating epileptic discharges and whose removal is necessary for complete abolition of seizures [3]. High-frequency oscillations (HFOs) have been evaluated as a specific biomarker for epileptogenicity in iEEG [5,6,7] These fast oscillations (FOs) appear in the frequency range of 80–500 Hz, classified into ripples (80–200 Hz) and fast ripples (200–500 Hz), and are defined as spontaneous patterns above the baseline, clearly distinguished from noise and with at least four oscillations [8]. The relationship between them is still an open discussion, HFOs seem to be more specific to delimitate the EZ than IEDs [5]
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