Learning from Non-Seizure Clusters for EEG Analytics

Javad Birjandtalab, Melvin James, Mehrdad Nourani, Jay H Harvey

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

Abstract

EEG data collected in EMU is highly imbalanced and accuracy of automatic epileptic seizure detection is naturally low. Our aim is to increase the accuracy by reducing the imbalance ratio of seizure and non-seizure classes. We hypothesis that the non-seizure class itself includes various daily brain activities and then the data points are distributed as clusters in this class. In training phase, we propose a technique to cluster the majority (non-seizure) class into k clusters. Then, we train k KNN classifiers using each of k non-seizure clusters plus seizure class. In testing phase, we classify an incoming sample using this model and the non-seizure cluster closest to the incoming sample. We employed a state-of-the-art visualization technique to illustrate clusters of majority non-seizure class in two dimensions. The results, applied to MIT EEG dataset, show that our technique provides a higher average F-Measure accuracy.

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
CountryUnited States
CityCleveland
Period10/17/1810/19/18

Keywords

  • Clustering
  • Epilepsy
  • Imbalance Data
  • Majority Class
  • Spectral Analysis

ASJC Scopus subject areas

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

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  • Cite this

    Birjandtalab, J., James, M., Nourani, M., & Harvey, J. H. (2018). Learning from Non-Seizure Clusters for EEG Analytics. In 2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings [8584837] (2018 IEEE Biomedical Circuits and Systems Conference, BioCAS 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIOCAS.2018.8584837