Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models

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 approac...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2017-12, Vol.64 (12), p.2988-2996
Main Authors: Vijayakumar, Vishal, Case, Michelle, Shirinpour, Sina, He, Bin
Format: Article
Language:English
Subjects:
Biomarkers
Chronic pain
Cingulate cortex
Classification
EEG
Electroencephalography
electroencephalography (EEG)
Frequencies
gamma oscillations
Learning algorithms
Machine learning
Mathematical models
Pain
pain quantification
State of the art
Time-frequency analysis
Wavelet analysis
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Summary: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.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2017.2756870