Estimation of seizure onset zone from ictal scalp EEG using independent component analysis in extratemporal lobe epilepsy. (1st April 2022)

• Record Type: Journal Article
• Authors: de Borman, Aurélie; Vespa, Simone; Tahry, Riëm El; Absil, P.-A.
• Is Part Of: Journal of neural engineering. Volume 19:Number 2(2022)
• Publication Date: 2022-04-01
• Subjects: independent component analysis (ICA) -- epilepsy -- seizure onset zone -- electroencephalogram (EEG) -- source imaging; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac55ad
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective. The purpose of this study is to localize the seizure onset zone of patients suffering from drug-resistant epilepsy. During the last two decades, multiple studies proposed the use of independent component analysis (ICA) to analyze ictal electroencephalogram (EEG) recordings. This study aims at evaluating ICA potential with quantitative measurements. In particular, we address the challenging step where the components extracted by ICA of an ictal nature must be selected. Approach. We considered a cohort of 10 patients suffering from extratemporal lobe epilepsy who were rendered seizure-free after surgery. Different sets of pre-processing parameters were compared and component features were explored to help distinguish ictal components from others. Quantitative measurements were implemented to determine whether some of the components returned by ICA were located within the resection zone and thus likely to be ictal. Finally, an assistance to the component selection was proposed based on the implemented features. Main results. For every seizure, at least one component returned by ICA was localized within the resection zone, with the optimal pre-processing parameters. Three features were found to distinguish components localized within the resection zone: the dispersion of their active brain sources, the ictal rhythm power and the contribution to the EEG variance. Using the implemented component selection assistance based on the features, the probability that the first proposed component yields an accurate estimation reaches 51.43% (without assistance: 24.74%). The accuracy reaches 80% when considering the best result within the first five components. Significance. This study confirms the utility of ICA for ictal EEG analysis in extratemporal lobe epilepsy, and suggests relevant features to analyze the components returned by ICA. A component selection assistance is proposed to guide clinicians in their choice for ictal components.