skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Deep Recurrent Neural Networks for seizure detection and early seizure detection systems

Abstract

Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizure detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.

Authors:
 [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1366924
Report Number(s):
LLNL-TR-732479
DOE Contract Number:
AC52-07NA27344
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE

Citation Formats

Talathi, S. S.. Deep Recurrent Neural Networks for seizure detection and early seizure detection systems. United States: N. p., 2017. Web. doi:10.2172/1366924.
Talathi, S. S.. Deep Recurrent Neural Networks for seizure detection and early seizure detection systems. United States. doi:10.2172/1366924.
Talathi, S. S.. Mon . "Deep Recurrent Neural Networks for seizure detection and early seizure detection systems". United States. doi:10.2172/1366924. https://www.osti.gov/servlets/purl/1366924.
@article{osti_1366924,
title = {Deep Recurrent Neural Networks for seizure detection and early seizure detection systems},
author = {Talathi, S. S.},
abstractNote = {Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizure detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.},
doi = {10.2172/1366924},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jun 05 00:00:00 EDT 2017},
month = {Mon Jun 05 00:00:00 EDT 2017}
}

Technical Report:

Save / Share: