Automatic detection and visualisation of MEG ripple oscillations in epilepsy

van Klink, Nicole, van Rosmalen, Frank, Nenonen, Jukka, Burnos, Sergey, Helle, Liisa, Taulu, Samu, Furlong, Paul Lawrence, Zijlmans, Maeike and Hillebrand, Arjan (2017). Automatic detection and visualisation of MEG ripple oscillations in epilepsy. NeuroImage: Clinical, 15 , pp. 689-701.

Abstract

High frequency oscillations (HFOs, 80–500 Hz) in invasive EEG are a biomarker for the epileptic focus. Ripples (80–250 Hz) have also been identified in non-invasive MEG, yet detection is impeded by noise, their low occurrence rates, and the workload of visual analysis. We propose a method that identifies ripples in MEG through noise reduction, beamforming and automatic detection with minimal user effort. We analysed 15 min of presurgical resting-state interictal MEG data of 25 patients with epilepsy. The MEG signal-to-noise was improved by using a cross-validation signal space separation method, and by calculating ~ 2400 beamformer-based virtual sensors in the grey matter. Ripples in these sensors were automatically detected by an algorithm optimized for MEG. A small subset of the identified ripples was visually checked. Ripple locations were compared with MEG spike dipole locations and the resection area if available. Running the automatic detection algorithm resulted in on average 905 ripples per patient, of which on average 148 ripples were visually reviewed. Reviewing took approximately 5 min per patient, and identified ripples in 16 out of 25 patients. In 14 patients the ripple locations showed good or moderate concordance with the MEG spikes. For six out of eight patients who had surgery, the ripple locations showed concordance with the resection area: 4/5 with good outcome and 2/3 with poor outcome. Automatic ripple detection in beamformer-based virtual sensors is a feasible non-invasive tool for the identification of ripples in MEG. Our method requires minimal user effort and is easily applicable in a clinical setting.

Publication DOI: https://doi.org/10.1016/j.nicl.2017.06.024
Divisions: Life & Health Sciences > Psychology
Life & Health Sciences
Life & Health Sciences > Clinical and Systems Neuroscience
Life & Health Sciences > Aston Brain Centre
Additional Information: © 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).
Uncontrolled Keywords: automatic detection,beamformer,epilepsy,high frequency oscillations,magnetoencephalography,virtual sensors,Radiology Nuclear Medicine and imaging,Neurology,Clinical Neurology,Cognitive Neuroscience
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2017
Authors: van Klink, Nicole
van Rosmalen, Frank
Nenonen, Jukka
Burnos, Sergey
Helle, Liisa
Taulu, Samu
Furlong, Paul Lawrence ( 0000-0002-9840-8586)
Zijlmans, Maeike
Hillebrand, Arjan

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