Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel
Nenhuma Miniatura disponível
Data
2019-01-21
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Tipo
Trabalho apresentado em evento
Direito de acesso
Acesso aberto
Resumo
Predicting epileptic seizure occurrence has long been a goal of the community surrounding it. Accurate prediction, however, is still elusive. This work presents a modified pipeline for the training of seizure prediction systems which aims to attenuate the effects of current data labeling strategies - and consequent data mislabeling of samples that heavily affect classifiers that are trained on it. This paper also presents a seizure prediction system trained following the proposed pipeline, which improved our system's performance by reducing its time-in-warning (TiW) by over 14%, while improving its prediction sensitivity to 72.4%, bringing its performance closer to the state-of-the-art performance (83.1% prediction sensitivity) for systems with similar TiW(41%) [1], while only requiring input from two scalp EEG electrodes-without making use of any variables external to the single EEG channels.
Descrição
Palavras-chave
Idioma
Inglês
Como citar
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, p. 2628-2634.