Neural Computing and Applications

, Volume 29, Issue 8, pp 47–57 | Cite as

A novel approach for automated detection of focal EEG signals using empirical wavelet transform

  • Abhijit Bhattacharyya
  • Manish Sharma
  • Ram Bilas Pachori
  • Pradip Sircar
  • U. Rajendra Acharya
New Trends in data pre-processing methods for signal and image classification


The determination of epileptogenic area is a prime task in presurgical evaluation. The seizure activity can be prevented by operating the affected areas by clinical surgery. In this paper, an automatic approach has been presented to detect electroencephalogram (EEG) signals of non-focal and focal groups. The proposed approach can be used to determine the area linked to the focal epilepsy. In our method, the EEG signal is decomposed into rhythms using empirical wavelet transform technique. The two-dimensional (2D) projections of the reconstructed phase space (RPS) have been obtained for the rhythms. Area measures for various RPS plots are estimated using central tendency measure (CTM) parameter. The area parameters are used with least-squares support vector machine (LS-SVM) classifier to classify the focal and non-focal classes of EEG signals. In this work, we have achieved a maximum classification accuracy of 90%, sensitivity and specificity of 88 and 92%, respectively, using 50 pairs of focal and non-focal EEG signals. The same method has achieved maximum classification accuracy, sensitivity and specificity of 82.53, 81.60 and 83.46%, respectively, with 750 pairs of signals. The developed prototype can be used for the epileptic patients and aid the clinicians to confirm diagnosis.


Focal EEG signal Empirical wavelet transform Reconstructed phase space Central tendency measure Least-squares support vector machine 


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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Abhijit Bhattacharyya
    • 1
  • Manish Sharma
    • 1
  • Ram Bilas Pachori
    • 1
  • Pradip Sircar
    • 2
  • U. Rajendra Acharya
    • 3
    • 4
    • 5
  1. 1.Discipline of Electrical EngineeringIndian Institute of Technology IndoreIndoreIndia
  2. 2.Department of Electrical EngineeringIndian Institute of Technology KanpurKanpurIndia
  3. 3.Department of Electronics and Computer EngineeringNgee Ann PolytechnicSingaporeSingapore
  4. 4.Department of Biomedical Engineering, School of Science and TechnologySIM UniversitySingaporeSingapore
  5. 5.Department of Biomedical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia

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