Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes.

Verhoeven, Thibault; Coito, Ana; Plomp, Gijs; Thomschewski, Aljoscha; Pittau, Francesca; Trinka, Eugen; Wiest, Roland; Schaller, Karl; Michel, Christoph; Seeck, Margitta; Dambre, Joni; Vulliemoz, Serge; van Mierlo, Pieter (2018). Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes. NeuroImage: Clinical, 17, pp. 10-15. Elsevier 10.1016/j.nicl.2017.09.021

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Objective To diagnose and lateralise temporal lobe epilepsy (TLE) by building a classification system that uses directed functional connectivity patterns estimated during EEG periods without visible pathological activity. Methods Resting-state high-density EEG recording data from 20 left TLE patients, 20 right TLE patients and 35 healthy controls was used. Epochs without interictal spikes were selected. The cortical source activity was obtained for 82 regions of interest and whole-brain directed functional connectivity was estimated in the theta, alpha and beta frequency bands. These connectivity values were then used to build a classification system based on two two-class Random Forests classifiers: TLE vs healthy controls and left vs right TLE. Feature selection and classifier training were done in a leave-one-out procedure to compute the mean classification accuracy. Results The diagnosis and lateralization classifiers achieved a high accuracy (90.7% and 90.0% respectively), sensitivity (95.0% and 90.0% respectively) and specificity (85.7% and 90.0% respectively). The most important features for diagnosis were the outflows from left and right medial temporal lobe, and for lateralization the right anterior cingulate cortex. The interaction between features was important to achieve correct classification. Significance This is the first study to automatically diagnose and lateralise TLE based on EEG. The high accuracy achieved demonstrates the potential of directed functional connectivity estimated from EEG periods without visible pathological activity for helping in the diagnosis and lateralization of TLE.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

UniBE Contributor:

Wiest, Roland


600 Technology > 610 Medicine & health








Martin Zbinden

Date Deposited:

23 Apr 2018 09:22

Last Modified:

23 Apr 2018 09:32

Publisher DOI:


PubMed ID:


Uncontrolled Keywords:

Diagnosis EEG Lateralization Machine learning Temporal lobe epilepsy




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