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bioRxiv · 10.1101/361113

Towards fast and reliable simultaneous EEG-fMRI analysis of epilepsy with automatic spike detection

Omidvarnia, A.·Kowalczyk, M.·Pedersen, M.·Jackson, G.
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Abstract

ObjectiveThe process of manually marking up epileptic spikes for simultaneous electroencephalogram (EEG) and resting state functional MRI (rsfMRI) analysis in epilepsy studies is a tedious and subjective task for a human expert. The aim of this study was to evaluate whether automatic EEG spike detection can facilitate EEG-rsfMRI analysis, and to assess its potential as a clinical tool in epilepsy.\n\nMethodsWe implemented a fast algorithm for detection of uniform interictal epileptiform discharges (IEDs) in one-hour scalp EEG recordings of 19 refractory focal epilepsy datasets (from 16 patients) who underwent a simultaneous EEG-rsfMRI recording. Our method was based on matched filtering of an IED template (derived from human markup) used to automatically detect other similar EEG events. We comprehensively compared simultaneous EEG-rsfMRI results between automatic IED detection and standard analysis with human EEG markup only.\n\nResultsIn contrast to human markup, automatic IED detection takes a much shorter time to detect IEDs and export an output text file containing spike timings. In 13/19 focal epilepsy cases, statistical EEG-rsfMRI maps based on automatic spike detection method were comparable with human markup, and in 6/19 focal epilepsy cases it revealed additional brain regions not seen with human EEG markup. Additional events detected by our automated method independently revealed similar patterns of activation to a human markup. Overall, automatic IED detection provides greater statistical power in EEG-rsfMRI analysis compared to human markup in a short timeframe.\n\nConclusionsAutomatic spike detection is a simple and fast method that can reproduce comparable and, in some cases, even superior results compared to the common practice of manual EEG markup in EEG-rsfMRI analysis of epilepsy.\n\nSignificanceOur study shows that IED detection algorithms can be effectively used in epilepsy clinical settings. This work further helps in translating EEG-rsfMRI research into a fast, reliable and easy-to-use clinical tool for epileptologists. Our IED detection approach will be publicly available as a MATLAB package at: https://github.com/omidvarnia/Automatic_focal_spike_detection.\n\nHighlightsO_LIAutomatic spike detection increases the number of detected uniform epileptic interictal discharges and enhances statistical power of EEG-rsfMRI inter-subject variability maps,\nC_LIO_LIAutomatic spike detection can identify additional activated brain regions with presumed epileptogenic focus not seen in standard analysis based on human markup,\nC_LIO_LIAutomatic spike detection can shorten the IED identification process.\nC_LI

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Omidvarnia, A., Kowalczyk, M., Pedersen, M., Jackson, G.. 2018-07-12. Towards fast and reliable simultaneous EEG-fMRI analysis of epilepsy with automatic spike detection. https://doi.org/10.1101/361113

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