Automated epilepsy detection techniques from electroencephalogram signals: a review study

Supriya Supriya, Siuly Siuly, Hua Wang, Yanchun Zhang

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

Epilepsy is a serious neurological condition which contemplates as top 5 reasons for avoidable mortality from ages 5–29 in the worldwide. The avoidable deaths due to epilepsy can be reduced by developing efficient automated epilepsy detection or prediction machines or software. To develop an automated epilepsy detection framework, it is essential to properly understand the existing techniques and their benefit as well as detriment also. This paper aims to provide insight on the information about the existing epilepsy detection and classification techniques as they are crucial for supporting clinical-decision in the course of epilepsy treatment. This review study accentuate on the existing epilepsy detection approaches and their drawbacks. This information presented in this article will be helpful to the neuroscientist, researchers as well as to technicians for assisting them in selecting the reliable and appropriate techniques for analyzing epilepsy and developing an automated software system of epilepsy identification.

Original languageEnglish
Article number33
JournalHealth Information Science and Systems
Volume8
Issue number1
DOIs
Publication statusPublished - 1 Dec 2020
Externally publishedYes

Keywords

  • Classification
  • EEG
  • Epilepsy
  • Feature extraction
  • Machine learning
  • Time–frequency

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