Epileptic EEG signal classifications based on DT-CWT and SVM classifier

  • S. Deivasigamani Faculty of Engineering and Computer Technology, AIMST University, Malaysia-08100
  • G. Narmadha Department of Electrical & Electronics Engineering, Sethu Institute of Technology, India.
  • P.K.Rajesh Faculty of Medicine, AIMST University, Malaysia-08100
  • C. Senthilpari Multimedia University, Malaysia-63100
  • Wong Hin Yong Multimedia University, Malaysia-63100


Contamination in human cerebrum causes the mind issue which is as Epilepsy. The contaminated territory in the cerebrum area creates the unpredictable example signals as focal signs and the other sound locales in the mind produce the standard example signals as non-focal sign. Henceforth, the discovery of focal signs from the non-focal signs is a significant for epileptic medical procedure in epilepsy patients. This paper proposes a straightforward and proficient technique for EEG signals orders utilizing SVM classifier. The exhibition of the proposed EEG signals characterization framework is assessed as far as Sensitivity, Specificity, and Accuracy.


Key words: Epilepsy; Focal; SVM; Neural networks; Epileptogenic area.

Computer Engineering