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

Abstract

ObjectiveTo 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.MethodsResting-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.ResultsThe 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.SignificanceThis 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.

Altered neural synchronization to pure tone stimulation in patients with mesial temporal lobe epilepsy: An MEG study

“…For lateralization, the accuracy was 92%, sensitivity was 92%, and specificity was 93%. These results are comparable to those of a recent study [33] in which machine learning techniques were used to obtain the diagnosis (accuracy 91%), and lateralization classifier (accuracy 90%) on the basis of resting-state EEG data.…”

Interictal epileptogenic zone localization in patients with focal epilepsy using electric source imaging and directed functional connectivity from low‐densityEEG

“…EEG preprocessing, epoch segmentation, and MRI parcellation were done using the freely available software Cartool (https://sites.google.com/site/cartoolcommunity/). EEG source imaging comprising the inverse solution with sLORETA and the FDM head model was computed using software written in Matlab (Release 2012b, The MathWorks, Inc., Natick, MA), like in our previous studies . The Matlab code has entered Epilog NV (Ghent, Belgium).…”

“…EEG source imaging comprising the inverse solution with sLORETA and the FDM head model was computed using software written in Matlab (Release 2012b, The MathWorks, Inc., Natick, MA), like in our previous studies. 16,30 The Matlab code has entered Epilog NV (Ghent, Belgium). Connectivity estimation was performed equally with in house software written in Matlab, like in our previous studies.…”

Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised

“…Such approaches hold further potential in providing support in diagnosing and lateralising epilepsy outside of the standard, clinical environment. 36 Here, we have not attempted to take into account whether the subjects with focal epilepsy had secondarily generalization or not; a new and larger set of data would allow us to examine whether these methods are sensitive to differentiating subjects whose focal seizures generalize secondarily very rapidly from those for who this process occurs slower. Additionally, useable data for our study was limited by the fact that an EEG-trained clinician was required to select the EEG epochs for further analysis.…”