Hybrid machine learning method for a connectivity-based epilepsy diagnosis with resting-state EEG.

This study investigates the performance of a convolutional neural network (CNN) algorithm on epilepsy diagnosis. Without pathology, diagnosis involves long and costly electroencephalographic (EEG) monitoring. Novel approaches may overcome this by comparing brain connectivity using graph metrics. This study, however, uses deep learning to learn connectivity patterns directly from easily acquired EEG data. A CNN algorithm was applied on directed Granger causality (GC) connectivity measures, derived from 50 s of resting-state surface EEG recordings from 30 subjects with epilepsy and a 30 subject control group. The trained CNN filters reflected reduced delta band connectivity in frontal regions and increased left lateralized frontal-posterior gamma band connectivity. A diagnosis accuracy of 85% (F1 score 85%) was achieved by an ensemble of CNN models, each trained on differently prepared data from different electrode combinations. Appropriate preparation of connectivity data enables generic CNN algorithms to be used for detection of multiple discriminative epileptic features. Differential patterns revealed in this study may help to shed light on underlying altered cognitive abilities in epilepsy patients. The accuracy achieved in this study shows that, in combination with other methods, this approach could prove a valuable clinical decision support system for epilepsy diagnosis. 1: EEG measurements and subsequent connectivity calculation, 2: training of a neural network on resulting connectivity matrices, 3: extraction of most efficient CNN filters, which are neuromarker for epilepsy.