Epileptic Seizure Detection from Multivariate Temporal Data Using Gated Recurrent Unit

Abstract

The advancement in sensor and satellite technologies, biomedical informatics, climate informatics, and health care has led to the emergence of multivariate temporal data. Multivariate temporal data contains multiple time series with complex temporal behaviors. Mining Knowledge from such complex data remains challenging area of research. This paper primarily focuses on developing temporal decision support system in medical (TDSSM) to detect epileptic seizure from multivariate temporal data. This work uses deep neural networks to build temporal classification model for epileptic seizure detection. The temporal model for epilepsy seizure detection is constructed by training the gated recurrent unit using multivariate temporal dependencies of time series observations acquired from the EEG recording of 500 individuals.