Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models
Year: 2017
Type: article
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.
Institution: University of Minnesota
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Cannabinoid-based therapy and approaches to quantify pain in sickle cell disease, MRI-compatible integrated NIRS/EEG system for applications to Clinical Neuroscience, G2221576032, Mind-body awareness training and brain-computer interface, G6224859938, IGERT: Interacting with the Brain: Mechanisms, Optimization, and Innovation, G9000101006, G9000168722
OpenAlex ID: W2757765264