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Identification of Epileptic Seizures from Scalp EEG Signals Based on TQWT

  • Abhijit BhattacharyyaEmail author
  • Lokesh Singh
  • Ram Bilas Pachori
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

In this work, we propose a method for epileptic seizure detection from scalp electroencephalogram (EEG) signals. The proposed method is based on the application of tunable-Q wavelet transform (TQWT). The long duration scalp EEG signals have been segmented into one-second duration segments using a moving window-based scheme. After that, TQWT has been applied in order to decompose scalp EEG signals segments into multiple sub-band signals of different oscillatory levels. We have generated two-dimensional (2D) reconstructed phase space (RPS) plot of each of the sub-band signals. Further, the central tendency measure (CTM) has been applied in order to measure the area of the 2D-RPS plots. These computed area measures have been used as features for distinguishing seizure and seizure-free EEG signal segments. Finally, we have used a feature-processing technique which clearly discriminates epileptic seizures in the scalp EEG signals. The proposed method may also find application in the online detection of epileptic seizures from intracranial EEG signals.

Keywords

TQWT RPS CTM Scalp EEG signal Epileptic seizure detection 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Abhijit Bhattacharyya
    • 1
    Email author
  • Lokesh Singh
    • 1
  • Ram Bilas Pachori
    • 1
  1. 1.Discipline of Electrical EngineeringIndian Institute of Technology IndoreIndoreIndia

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