AUTHOR=Costa Leonardo R. da , Campos Brunno M. de , Alvim Marina K. M. , Castellano Gabriela 

TITLE=RETRACTED: EEG Signal Connectivity for Characterizing Interictal Activity in Patients With Mesial Temporal Lobe Epilepsy

JOURNAL=Frontiers in Neurology

VOLUME=Volume 12 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2021.673559

DOI=10.3389/fneur.2021.673559

ISSN=1664-2295

ABSTRACT=Over the last decade, several methods for analysis of epileptiform signals in electroencephalography (EEG) have been proposed. These methods have mainly used EEG signal features in either the time or the frequency domain to separate regular, interictal and ictal activity. The aim of this work was to evaluate the feasibility of using functional connectivity (FC) feature extraction methods for the analysis of epileptiform discharges in EEG signals. These signals were obtained from EEG-fMRI sessions of 10 patients with mesial temporal lobe epilepsy (MTLE) with unilateral hippocampal atrophy. The connectivity functions investigated were motif comparison, imaginary coherence and magnitude squared coherence, in the alpha, beta and gamma bands of the EEG. EEG signals were sectioned into 1 s epochs and classified according to (using neurologist markers): activity far from interictal epileptiform discharges (IED), activity immediately before an IED and, finally, mid-IED activity. Graph theory was used to build connectivity matrices for each epoch, for each FC function. The statistical distributions of the graph properties (mean degree, efficiency, eigenvector centrality and cluster coefficient) were compared among the three classes, for each FC function. We found significant differences (p<0.02, ANOVA test) between the graph metrics of the mid class compared to before and far, for motif comparison. This analysis demonstrated the potential of FC measures, computed using motif comparison, for the characterization of epileptiform activity of MTLE patients.