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Epileptic Seizure Detection from Multivariate Temporal Data Using Gated Recurrent Unit

  • Saranya Devi JeyabalanEmail author
  • Nancy Jane Yesudhas
Conference paper
  • 20 Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 127)

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.

Keywords

Epileptic seizure Temporal classification model Gated recurrent unit 

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.Madras Institute of TechnologyAnna UniversityChennaiIndia

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