We selected all patients undergoing 30-minute EEG at the CHUM in 2018. Exclusion criteria were absence of follow-up after EEG, unclear epilepsy diagnosis at last follow-up, and electrical seizure(s) on EEG. Medical charts were reviewed for seizure freedom after EEG. EEGs were segmented into non-overlapping 10s windows. Several features were extracted for each electrode and each time window, each feature capturing a distinct linear or non-linear property of the EEG signal. The extracted features were: band power, peak alpha frequency, Hurst exponent, entropy (ApEn, SampEn, MsEn, SpecEn, FuzzEn, and PermEn), line length, and correlation dimension. We trained a regularized boosted trees machine learning model on multichannel EEG segments to predict seizure freedom during follow-up. We averaged predictions over windows to obtain one prediction per EEG. We tuned hyperparameters with cross-validation on a validation ensemble (80% of data) and evaluated the model on a testing set (20% of data).