%0 Journal Article
%T TOWARDS INTERPRETABLE SEIZURE DETECTION USING WEARABLES.
%A Al-Hussaini I
%A Mitchell CS
%J Proc IEEE Int Conf Acoust Speech Signal Process
%V 2023
%N 0
%D 2023 Jun
%M 38682049
暂无%R 10.1109/icassp49357.2023.10097091
%X Seizure detection using machine learning is a critical problem for the timely intervention and management of epilepsy. We propose SeizFt, a robust seizure detection framework using EEG from a wearable device. It uses features paired with an ensemble of trees, thus enabling further interpretation of the model's results. The efficacy of the underlying augmentation and class-balancing strategy is also demonstrated. This study was performed for the Seizure Detection Challenge 2023, an ICASSP Grand Challenge.