Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm

Scot Davidson, Niamh McCallan, Kok Yew Ng, Pardis Biglarbeigi, Dewar Finlay, Boon Leong Lan, James McLaughlin

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

3 Citations (Scopus)

Abstract

Epilepsy affects 50 million people worldwide and is one of the most common serious neurological disorders. Seizure detection and classification is a valuable tool for diagnosing and maintaining the condition. An automated classification algorithm will allow for accurate diagnosis. Utilising the Temple University Hospital (TUH) Seizure Corpus, six seizure types are compared; absence, complex partial, myoclonic, simple partial, tonic and tonicclonic models. This study proposes a method that utilises unique features with a novel parallel classifier - Parallel Genetic Naive Bayes (NB) Seizure Classifier (PGNBSC). The PGNBSC algorithm searches through the features and by reclassifying the data each time, the algorithm will create a matrix for optimum search criteria. Ictal states from the EEGs are segmented into 1.8 s windows, where the epochs are then further decomposed into 13 different features from the first intrinsic mode function (IMF). The features are compared using an original NB classifier in the first model. This is improved upon in a second model by using a genetic algorithm (Binary Grey Wolf Optimisation, Option 1) with a NB classifier. The third model uses a combination of the simple partial and complex partial seizures to provide the highest classification accuracy for each of the six seizures amongst the three models (20%, 53%, and 85% for first, second, and third model, respectively).

Original languageEnglish
Title of host publicationMELECON 2022 - IEEE Mediterranean Electrotechnical Conference, Proceedings
PublisherIEEE
Pages430-435
Number of pages6
ISBN (Electronic)9781665442800
ISBN (Print)9781665442817
DOIs
Publication statusPublished - 3 Aug 2022
Event21st IEEE Mediterranean Electrotechnical Conference, MELECON 2022 - Palermo, Italy
Duration: 14 Jun 202216 Jun 2022

Publication series

Name
ISSN (Print)2158-8473
ISSN (Electronic)2158-8481

Conference

Conference21st IEEE Mediterranean Electrotechnical Conference, MELECON 2022
Country/TerritoryItaly
CityPalermo
Period14/06/2216/06/22

Keywords

  • Classification
  • Electroencephalography (EEG)
  • Epileptic Seizure
  • Genetic Algorithm
  • Naive Bayes

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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