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.
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.
Authors: Andre Marquand; Matthew Howard; Michael Brammer; Carlton Chu; Steven Coen; Janaina Mourão-Miranda Journal: Neuroimage Date: 2009-10-29 Impact factor: 6.556
Authors: Javeria A Hashmi; Marwan N Baliki; Lejian Huang; Alex T Baria; Souraya Torbey; Kristina M Hermann; Thomas J Schnitzer; A Vania Apkarian Journal: Brain Date: 2013-09 Impact factor: 13.501
Authors: Enrico Schulz; Elisabeth S May; Martina Postorino; Laura Tiemann; Moritz M Nickel; Viktor Witkovsky; Paul Schmidt; Joachim Gross; Markus Ploner Journal: Cereb Cortex Date: 2015-03-08 Impact factor: 5.357
Authors: Joshua Levitt; Muhammad M Edhi; Ryan V Thorpe; Jason W Leung; Mai Michishita; Suguru Koyama; Satoru Yoshikawa; Keith A Scarfo; Alexios G Carayannopoulos; Wendy Gu; Kyle H Srivastava; Bryan A Clark; Rosana Esteller; David A Borton; Stephanie R Jones; Carl Y Saab Journal: Neuroimage Date: 2020-08-29 Impact factor: 6.556