Rebalancing Techniques for Asynchronously Distributed EEG Data to Improve Automatic Seizure Type Classification

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

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

1 Citation (Scopus)

Abstract

Epilepsy, a nervous system disorder, is charac-terised by unprovoked, unpredictable, and recurrent seizures. To diagnose epileptic seizures, electroencephalography (EEG) is frequently used in medical settings. Effective automated detection and classification strategies are needed because visual analysis and interpretation of EEG signals consume time and call for specialised expertise. The main objective of this paper is to examine the effectiveness of multiple rebalancing techniques to address the problem of asynchronously distributed data, specifically employing random resampling, synthetic minority oversampling technique (SMOTE), and adaptive synthetic sampling approach for imbalanced learning (ADASYN), for seizure type classification. The model utilises both frequency information using variational mode decomposition (VMD), and phase information by extracting the phase locking value (PLV) across 19 common EEG channels found in the Temple University Hospital EEG Seizure Corpus (TUSZ) v1.5.2 dataset. The random subspace k-nearest neighbour (RSkNN) ensemble classifier is used for seizure type classification of five classes - complex partial seizures (CPSZ), simple partial seizures (SPSZ), absence seizures (ABSZ), tonic clonic seizures (TCSZ), and tonic seizures (TNSZ) - to determine the performance of each rebalancing techniques, with the highest accuracy and weighted F1 score of 96.28% and 0.964, respectively using SMOTE with two nearest neighbours.

Original languageEnglish
Title of host publication2023 57th Annual Conference on Information Sciences and Systems, CISS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665451819
DOIs
Publication statusPublished - 10 Apr 2023
Event57th Annual Conference on Information Sciences and Systems, CISS 2023 - Johns Hopkins University, Baltimore, United States
Duration: 22 Mar 202324 Mar 2023
Conference number: 57th

Publication series

Name2023 57th Annual Conference on Information Sciences and Systems, CISS 2023

Conference

Conference57th Annual Conference on Information Sciences and Systems, CISS 2023
Country/TerritoryUnited States
CityBaltimore
Period22/03/2324/03/23

Keywords

  • ADASYN
  • Classification
  • Electroencephalography
  • Epileptic seizure
  • Machine learning
  • SMOTE

ASJC Scopus subject areas

  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Artificial Intelligence
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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