Deep learning and electrocorticography to tailor epilepsy surgery

Purpose:Intra- operative electrocorticography (ioECoG) is used to delineate epileptogenic tissue. This delineation can be difficult due to the paucity of accurate epileptic io-ECoG biomarkers. Computer- aided pattern recognition algorithms can be useful in the delineation process but need to be validated. This requires a large ioECoG data-set. Our aim is to construct a training and test set to train and validate a convolutional neural network (CNN) for binary classification of ioECoG channels as epileptic or non- epileptic.Methods:We retrospectively included patients who had an Engel 1A outcome from the RESPect database – a data-base with intracranial electroencephalography data from patients who underwent epilepsy surgery from 2008 on at University Medical Center Utrecht. Patients undergoing amygdala- hippocampectomy were excluded. The ioECoG channels were measured at 2048Hz and labelled as being inside or outside the resected area based on pre- and pos-tresection photos. All resected channels were assumed epileptic, given that all patients had Engel 1A outcome. We split the patients into an 80% training and 20% test set after stratification by age and pathology. The CNN will be trained and validated to dichotomously classify single ioECoG channels on the training and test set, respectively.Results:In total 113 patients (mean age: 18 [0- 68] years) were included where 57 had frontal lobe epilepsy, 43 tem-poral lobe epilepsy, and 13 another anatomical location; 112 patients showed MRI abnormalities; 49 patients had tumour tissue, 39 cortical development malformation, 20 other pathology types, and 6 no abnormalities confirmed by pathology. Per patient, 1- 6 pre- resection ioECoG re-cordings were made with 6- 36 electrodes where at least 1 electrode covered the resection area.Conclusion:This work is the first to build a large ioECoG dataset to train and validate a CNN. This dataset will be used to test whether a CNN can classify a single ioECoG channel as epileptic or non- epileptic