Abstract
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.
Original language | English (US) |
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Article number | 8049487 |
Pages (from-to) | 2988-2996 |
Number of pages | 9 |
Journal | IEEE Transactions on Biomedical Engineering |
Volume | 64 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2017 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
Keywords
- Cingulate cortex
- electroencephalography (EEG)
- gamma oscillations
- machine learning
- pain quantification