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

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

J Neural Eng. 2017 Mar 22;:

Authors: Migliorelli C, Alonso J, Romero S, Nowak R, Russi A, Mananas M

Abstract
OBJECTIVE: In epilepsy, high-frequency oscillations (HFOs) are considered events highly linked to the seizure onset zone (SOZ). The detection of HFOs in noninvasive signals such as scalp EEG and MEG is still a challenging task. The aim of this study was to automatize the detection of ripples in MEG signals reducing the high-frequency noise using beamformer-based virtual sensors (VS) and applying an automatic procedure exploring the time-frequency content of the detected events.
APPROACH: 200 seconds of MEG signals and simultaneous iEEG were selected in 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. The performance was evaluated by comparing with simultaneous iEEG signals.
MAIN RESULTS: For the nine subjects, ROIs were located within the seizure-generating lobes. Precision and sensitivity values were 79.18% and 68.88%, respectively, considering iEEG detected events as benchmark. 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 EZ and guide the 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.

PMID: 28327467 [PubMed - as supplied by publisher]

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