Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm

Niamh McCallan (Lead / Corresponding author), Scot Davidson, Kok Yew Ng, Pardis Biglarbeigi, Dewar Finlay, Boon Leong Lan, James McLaughlin

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

4 Citations (Scopus)

Abstract

Epilepsy is a disorder of the nervous system that can affect people of any age group. With roughly 50 million people worldwide diagnosed with the disorder, it is one of the most common neurological disorders. The electroencephalogram (EEG) is an indispensable tool for diagnosis of epileptic seizures in an ideal case, as brain waves from an epileptic patient would present distinct abnormalities. However, in real world situations there will often be biological and electrical noise interference, as well as the issue of a multi-channel signal, which introduce a great challenge for seizure detection and classification. For this study, the Temple University Hospital (TUH) EEG Seizure Corpus dataset was used. This paper proposes a novel channel selection method which isolates different frequency ranges within five channels. This is based upon the frequencies at which normal brain waveforms exhibit. A one second window was selected, with a 0.5 s overlap. Wavelet signal denoising was performed using Daubechies-4 wavelet decomposition. Thresholding was applied using minimax soft thresholding criteria. Filter banking was used to localise frequency ranges from five specific channels. Statistical features were then derived from the outputs. After performing bagged trees classification using 500 learners, a test accuracy of 0.82 was achieved.

Original languageEnglish
Title of host publication2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PublisherIEEE
Pages1269-1276
Number of pages8
ISBN (Electronic)9789881476890
ISBN (Print)9781665441629
Publication statusPublished - 3 Feb 2022
Event2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan
Duration: 14 Dec 202117 Dec 2021

Publication series

Name
ISSN (Print)2640-009X
ISSN (Electronic)2640-0103

Conference

Conference2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Country/TerritoryJapan
CityTokyo
Period14/12/2117/12/21

Keywords

  • Deep learning
  • Neurological diseases
  • Manuals
  • Interference
  • Information processing
  • Brain modeling
  • Wavelet analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Instrumentation

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