doi: 10.7763/IJET.2016.V8.917
Pain Level Measurement Using Discrete Wavelet Transform
Maryam Vatankhah
,
Amir Toliyat
- the Computer Science Department, Stevens Institute of Technology, New Jersey, USA
Abstract
Experimental pain has extensively been used as a tool for investigating neural mechanisms and the psychologicalfactors involved in pain processing. The detection of existenceand/or level of pain is vital when verbal information is notpresent e.g. for infants, disabled persons, anesthetized patientsand animals also. This study shows that there is a firm relation between Electroencephalogram (EEG) and chronic pain levelsand EEG can be used as a reliable tool for detecting, measuringand diagnosing pain levels in humans.This paper proposed a use of wavelet coherency in order toestimate the three pain levels and its usage as an index for pain measurement. Besides, wavelet coefficients are studied to show consistencies with EEG dynamic were extracted to provide the feature vector. A Hidden Markov Model (HMM) and a supportvector machine (SVM) scheme was used for pain levels classification. This study confirms the hypothesis that brainpattern under the chronic pain mental task is mapped on EEGand the dependency of brain patterns to EEG is possible and detectable.
Keywords
- Chronic pain index
- electroencephalogram
- SVM
- HMM
How to Cite
Maryam Vatankhah and Amir Toliyat, "Pain Level Measurement Using Discrete Wavelet Transform," International Journal of Engineering and Technology, vol. 8, no. 5, pp. 380-384, 2016. https://doi.org/ 10.7763/IJET.2016.V8.917
Copyright & License
Copyright © 2016 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).