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

Journal article


Omidvarnia, Amir, Kowalczyk, Magdalena A., Pedersen, Mangor and Jackson, Graeme D.. (2019). Towards fast and reliable simultaneous EEG-fMRI analysis of epilepsy with automatic spike detection. Clinical Neurophysiology. 130(3), pp. 368-378. https://doi.org/10.1016/j.clinph.2018.11.024
AuthorsOmidvarnia, Amir, Kowalczyk, Magdalena A., Pedersen, Mangor and Jackson, Graeme D.
Abstract

Objective
The 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.

Methods
We 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 compared simultaneous EEG-rsfMRI results between automatic IED detection and standard analysis with human EEG markup only.

Results
In 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 datasets, statistical EEG-rsfMRI maps based on automatic spike detection method were comparable with human markup, and in 6/19 focal epilepsy cases automatic spike detection 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.

Conclusions
Automatic 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.

Significance
Our 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.

KeywordsEEG; fMRI; focal epilepsy; spike detection; interictal discharge; matched filtering
Year2019
JournalClinical Neurophysiology
Journal citation130 (3), pp. 368-378
PublisherLippincott Williams & Wilkins
ISSN0736-0258
Digital Object Identifier (DOI)https://doi.org/10.1016/j.clinph.2018.11.024
Scopus EID2-s2.0-85060077845
Research or scholarlyResearch
Page range368-378
FunderNational Health and Medical Research Council (NHMRC)
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online17 Dec 2018
Publication process dates
Accepted22 Nov 2018
Deposited12 Jul 2021
Grant IDNHMRC/1091593
NHMRC/1060312
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