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Title: Towards fast and reliable simultaneous EEG-fMRI analysis of epilepsy with automatic spike detection.
Austin Authors: Omidvarnia, Amir;Kowalczyk, Magdalena A;Pedersen, Mangor;Jackson, Graeme D 
Affiliation: The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Victoria, Australia
Department of Neurology, Austin Health, Heidelberg, Victoria, Australia
Issue Date: 17-Dec-2018 2018-12-17
Publication information: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology 2018; 130(3): 368-378
Abstract: 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. 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. 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. 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. 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.
DOI: 10.1016/j.clinph.2018.11.024
PubMed URL: 30669013
Type: Journal Article
Subjects: EEG
Focal epilepsy
Interictal discharge
Matched filtering
Spike detection
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