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Developing a Tunable Q-Factor Wavelet Transform Based Algorithm for Epileptic EEG Feature Extraction

  • Hadi Ratham Al GhayabEmail author
  • Yan Li
  • Siuly
  • Shahab Abdulla
  • Paul Wen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10594)

Abstract

Brain signals refer to electroencephalogram (EEG) data that contain the most important information in the human brain, which are non-stationary and nonlinear in nature. EEG signals are a mixture of sustained oscillation and non-oscillatory transients that are difficult to deal with by linear methods. This paper proposes a new technique based on a tunable Q-factor wavelet transform (TQWT) and statistical method (SM), denoted as TQWT-SM, to analyze epileptic EEG recordings. Firstly, EEG signals are decomposed into different sub—bands by the TWQT method, which is parameterized by its Q-factor and redundancy. This approach depends on the resonance of signals, instead of frequency or scales as the Fourier and wavelet transforms do. Secondly, each type of the sub-band vector is divided into n windows, and 10 statistical features from each window are extracted. Finally all the obtained statistical features are forwarded to a k nearest neighbor (k-NN) classifier to evaluate the performance of the proposed TQWT-SM method. The TQWT-SM features extraction method achieves good experimental results for the seven different epileptic EEG binary-categories by the k-NN classifier, in terms of accuracy (Acc), Matthew’s correlation coefficient (MCC), and F score (F1). The outcomes of the proposed technique can assist the experts to detect epileptic seizures.

Keywords

Electroencephalography (EEG) Tunable Q-factor wavelet transform Statistical method k nearest neighbor 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hadi Ratham Al Ghayab
    • 1
    Email author
  • Yan Li
    • 1
  • Siuly
    • 2
  • Shahab Abdulla
    • 3
  • Paul Wen
    • 1
  1. 1.Faculty of Health, Engineering and SciencesUniversity of Southern QueenslandDarling HeightsAustralia
  2. 2.Centre for Applied Informatics, College of Engineering and ScienceVictoria UniversityMelbourneAustralia
  3. 3.Open Access College, Language CentreUniversity of Southern QueenslandDarling HeightsAustralia

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