Prediction of epilepsy surgery outcome using foramen ovale EEG - A machine learning approach.

INTRODUCTION: Patients with drug-resistant focal epilepsy may benefit from ablative or resective surgery. In presurgical work-up, intracranial EEG markers have been shown to be useful in identification of the seizure onset zone and prediction of post-surgical seizure freedom. However, in most cases, implantation of depth or subdural electrodes is performed, exposing patients to increased risks of complications. METHODS: We analysed EEG data recorded from a minimally invasive approach utilizing foramen ovale (FO) and epidural peg electrodes using a supervised machine learning approach to predict post-surgical seizure freedom. Power-spectral EEG features were incorporated in a logistic regression model predicting one-year post-surgical seizure freedom. The prediction model was validated using repeated 5-fold cross-validation and compared to outcome prediction based on clinical and scalp EEG variables. RESULTS: Forty-seven patients (26 patients with post-surgical 1-year seizure freedom) were included in the study, with 31 having FO and 27 patients having peg onset seizures. The area under the receiver-operating curve for post-surgical seizure freedom (Engel 1A) prediction in patients with FO onset seizures was 0.74 ± 0.23 using electrophysiology features, compared to 0.66 ± 0.22 for predictions based on clinical and scalp EEG variables (p < 0.001). The most important features for prediction were spectral power in the gamma and high gamma ranges. EEG data from peg electrodes was not informative in predicting post-surgical outcomes. CONCLUSION: In this hypothesis-generating study, a data-driven approach based on EEG features derived from FO electrodes recordings outperformed the predictive ability based solely on clinical and scalp EEG variables. Pending validation in future studies, this method may provide valuable post-surgical prognostic information while minimizing risks of more invasive diagnostic approaches.