Machine learning for predicting levetiracetam treatment response in temporal lobe epilepsy

•We provide a proof-of-concept pipeline for predicting the clinical response to anti-seizure medications in people with epilepsy.•Quantitative EEG modifications induced by Levetiracetam are predictive of seizure-freedom in patients with Temporal Lobe Epilepsy.•Machine-learning approaches using EEG signals will help to develop personalized models for anti-seizure treatment selection. To determine the predictive power for seizure-freedom of 19-channels EEG, measured both before and after three months the initiation of the use of Levetiracetam (LEV), in a cohort of people after a new diagnosis of temporal-lobe epilepsy (TLE) using a machine-learning approach. Twenty-three individuals with TLE were examined. We dichotomized clinical outcome into seizure-free (SF) and non-seizure-free (NSF) after two years of LEV. EEG effective power in different frequency bands was compared using baseline EEG (T0) and the EEG after three months of LEV therapy (T1) between SF and NSF patients. Partial Least Square (PLS) analysis was used to test and validate the prediction of the model for clinical outcome. A total of 152 features were extracted from the EEG recordings. When considering only the features calculated at T1, a predictive power for seizure-freedom (AUC = 0.750) was obtained. When employing both T0 and T1 features, an AUC = 0.800 was obtained. This study provides a proof-of-concept pipeline for predicting the clinical response to anti-seizure medications in people with epilepsy. Future studies may benefit from the pipeline proposed in this study in order to develop a model that can match each patient to the most effective anti-seizure medication.