Abstract—Experimental pain has extensively been used as a tool for investigating neural mechanisms and the psychological
factors involved in pain processing. The detection of existence
and/or level of pain is vital when verbal information is not
present e.g. for infants, disabled persons, anesthetized patients
and animals also. This study shows that there is a firm relation between Electroencephalogram (EEG) and chronic pain levels
and EEG can be used as a reliable tool for detecting, measuring
and diagnosing pain levels in humans.
This paper proposed a use of wavelet coherency in order to
estimate 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 support
vector machine (SVM) scheme was used for pain levels classification. This study confirms the hypothesis that brain
pattern under the chronic pain mental task is mapped on EEG
and the dependency of brain patterns to EEG is possible and detectable.
Index Terms—Chronic pain index, electroencephalogram,
SVM, HMM.
The authors are with the Computer Science Department, Stevens Institute of Technology, New Jersey, USA (e-mail: mvatankh@stevens.com,
amir.toliyat.a@ieee.org).
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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.
