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

• Autor(a) principal: Marques, Joao M. C.
• Data de Publicação: 2018
• Outros Autores: Cerdeira, Hilda A. [UNESP], Tanaka, Edgar, Vitor, Conrado de, Gomez, Paula, Zheng, H., Callejas, Z., Griol, D., Wang, H., Hu, X, Schmidt, H., Baumbach, J., Dickerson, J., Zhang, L.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/185428

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.