Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel

Nenhuma Miniatura disponível

Data

2019-01-21

Título da Revista

ISSN da Revista

Título de Volume

Editor

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

Como citar

Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, p. 2628-2634.