{Reference Type}: Journal Article
{Title}: TOWARDS INTERPRETABLE SEIZURE DETECTION USING WEARABLES.
{Author}: Al-Hussaini I;Mitchell CS;
{Journal}: Proc IEEE Int Conf Acoust Speech Signal Process
{Volume}: 2023
{Issue}: 0
{Year}: 2023 Jun
暂无{DOI}: 10.1109/icassp49357.2023.10097091
{Abstract}: 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.