Hierarchal online temporal and spatial EEG seizure detection

Amirsalar Mansouri, Sanjay Singh, Khalid Sayood

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

1 Scopus citations

Abstract

Seizures affect a significant portion of the world's population and while a small proportion of the seizures are easy to detect, the vast majority are subtle enough to require the expertise of a neurologist. In this paper, we present a multifaceted computational approach to detecting the presence and locality of a seizure using Electroencephalography (EEG) signals. We test the proposed approach using a variety of signals and demonstrate the efficacy of the approach.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Electro Information Technology, EIT 2017
PublisherIEEE Computer Society
Pages416-421
Number of pages6
ISBN (Electronic)9781509047673
DOIs
StatePublished - Sep 27 2017
Event2017 IEEE International Conference on Electro Information Technology, EIT 2017 - Lincoln, United States
Duration: May 14 2017May 17 2017

Publication series

NameIEEE International Conference on Electro Information Technology
ISSN (Print)2154-0357
ISSN (Electronic)2154-0373

Other

Other2017 IEEE International Conference on Electro Information Technology, EIT 2017
CountryUnited States
CityLincoln
Period5/14/175/17/17

Keywords

  • EEG
  • PSD
  • epilepsy
  • networks
  • nonspecific-patient detection
  • on-line detection
  • seizure

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Mansouri, A., Singh, S., & Sayood, K. (2017). Hierarchal online temporal and spatial EEG seizure detection. In 2017 IEEE International Conference on Electro Information Technology, EIT 2017 (pp. 416-421). [8053397] (IEEE International Conference on Electro Information Technology). IEEE Computer Society. https://doi.org/10.1109/EIT.2017.8053397