Imbalance Learning Using Neural Networks for Seizure Detection

Javad Birjandtalab, Vipul Nataraj Jarmale, Mehrdad Nourani, Jay Harvey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Around 1% of world's population suffer from epileptic seizures which can lead to injuries and even unexpected death. Making use of EEG signals, which are proven to be the best indicators of seizures, we aim to build an Artificial Neural Networks to classify seizure and non-seizure events. However, the limited availability of seizure events in the EEG data makes it difficult for the automatic classifiers in general to accurately classify seizure events. To improve this, we propose an imbalance learning approach to improve accuracy of highly imbalanced seizure dataset. Since each patient provides a different response to the seizure, we personalize the classification models in terms of training data and model parameters. The proposed imbalance learning method provides an average F-measure accuracy above 86% for Physionet MIT dataset.

Original languageEnglish (US)
Title of host publication2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538636039
DOIs
StatePublished - Dec 20 2018
Event2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Cleveland, United States
Duration: Oct 17 2018Oct 19 2018

Publication series

Name2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings

Conference

Conference2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018
Country/TerritoryUnited States
CityCleveland
Period10/17/1810/19/18

Keywords

  • Artificial Neural Network
  • EEG
  • Epileptic Seizure
  • Imbalance Ratio
  • Imbalanced Learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Health Informatics
  • Instrumentation
  • Signal Processing
  • Biomedical Engineering

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