Objective: Effective pain assessment and management strategies are needed to better manage pain. In addition to self-report, an objective pain assessment system can provide a more complete picture of the neurophysiological basis for pain. In this study, a robust and accurate machine learning approach is developed to quantify tonic thermal pain across healthy subjects into a maximum of ten distinct classes. Methods: A random forest model was trained to predict pain scores using time-frequency wavelet representations of independent components obtained from electroencephalography (EEG) data, and the relative importance of each frequency band to pain quantification is assessed. Results: The mean classification accuracy for predicting pain on an independent test subject for a range of 1-10 is 89.45%, highest among existing state of the art quantification algorithms for EEG. The gamma band is the most important to both intersubject and intrasubject classification accuracy. Conclusion: The robustness and generalizability of the classifier are demonstrated. Significance: Our results demonstrate the potential of this tool to be used clinically to help us to improve chronic pain treatment and establish spectral biomarkers for future pain-related studies using EEG.
Bibliographical noteFunding Information:
Manuscript received May 22, 2017; revised August 8, 2017 and September 8, 2017; accepted September 9, 2017. Date of publication September 26, 2017; date of current version November 20, 2017. This work was supported in part by the U.S. National Institutes of Health under Grant HL117664, Grant AT009263, Grant NS096761, Grant EB021027, and Grant OD021721 and in part by the U.S. National Science Foundation under Grant DGE-1069104. (Corresponding author: Bin He.) V. Vijayakumar is with the Department of Electrical and Computer Engineering, University of Minnesota.
- Cingulate cortex
- electroencephalography (EEG)
- gamma oscillations
- machine learning
- pain quantification