International Clinical Neuroscience Journal

Vol. 4 No. 4 (2017)

Detection of Focal Epileptic Seizure Using NIRS Signal Based on Discrete Wavelet Transform

International Clinical Neuroscience Journal, Vol. 4 No. 4 (2017), 8 October 2017 , Page 134-139

Abstract

Background: Despite the large number of research and significant advances in neuroscience, the hemodynamic activities of epilepsy have been rarely investigated due to high costs, need for contrast agents in fMRI and PET, lack of signals during epileptic seizure and un-portability of the equipment. Recently, Near-infrared spectroscopy (NIRS) system has attracted a large number of researchers. This system does not have the above-mentioned problems and provides a better temporal resolution than the other equipment; however, it cannot be compared to PET or fMRI, in terms of spatial resolution. The project was conducted with a feasibility study to detect epileptic seizures and extraction of epileptic dynamics using a time multiplex system at 2 wavelengths of 740 and 850 nm. Analyzing the frequency and temporal-domains of 8 patients with focal epilepsy in temporal area during the time of sleeping, we can identify the most difference between epileptic and normal conditions in low-frequencies at the high order Daubechies wavelet transform of hemodynamic components. The main challenge is the significant resemblances between epileptic dynamic and motion artifact in low frequencies. Finally, using the most appropriate features such as Shannon entropy and the new index that we named “upgraded cumulants” showing proper separability under t test and also by using different classifiers, the best result was achieved with the help of SVM classifier with an accuracy of 78.57%

 

Keywords:
  • Component
  • Detection
  • NIRs Signal
  • Epileptic Seizure
  • Daubechies wavelet
  • Upgraded cumulants
  • Shannon Entropy
  • SVM Classifier

How to Cite

1.
Modir A, Khalilzadeh MA, Gorji A. Detection of Focal Epileptic Seizure Using NIRS Signal Based on Discrete Wavelet Transform. Int Clin Neurosci J [Internet]. 2017 Oct. 8 [cited 2026 Jul. 5];4(4):134-9. Available from: https://journals.sbmu.ac.ir/neuroscience/article/view/19175

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