EEG Signal-based Epileptic Seizure Detection

  • M. Gokul
  • , L. Jagasri
  • , M. Sindhuja
  • , R. Barkavi
  • , Pradeep Murugesan
  • , T. Arun Prasath

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Epilepsy is a neurological condition marked by abnormal brain activity, which includes seizures. EEG data, which are recordings of brain activity, are frequently employed in a wide range of medical applications, including the identification of epileptic seizures. Neurologists used to draw conclusions by visual assessment. The findings, however, depended on the reader's level of skill and were frequently quite time-consuming. Thus, automatic epileptic seizure detection methods that can deliver accurate outcomes quickly are needed. Hence, this paper differentiates the normal and epileptic EEG waveforms with frequency estimation technique. Consequently, the frequency information of the non-epileptic and epileptic EEG is discovered, and the waveforms between them are easily distinguished.

Original languageEnglish
Title of host publicationInternational Conference on Automation, Computing and Renewable Systems, ICACRS 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages223-228
Number of pages6
ISBN (Electronic)9781665460842
DOIs
StatePublished - 1 Jan 2022
Externally publishedYes
Event1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022 - Pudukkottai, India
Duration: 13 Dec 202215 Dec 2022

Publication series

NameInternational Conference on Automation, Computing and Renewable Systems, ICACRS 2022 - Proceedings

Conference

Conference1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022
Country/TerritoryIndia
CityPudukkottai
Period13/12/2215/12/22

Keywords

  • Electroencephalogram (EEG)
  • Electroencephalogram signal database
  • epileptic seizures
  • time-frequency domain

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Renewable Energy, Sustainability and the Environment
  • Control and Optimization
  • Instrumentation

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