Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/226107
Title: Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models
Authors: Vishal Vijayakumar;Michelle Case;Sina Shirinpour;Bin He
Year: 2017
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/226107
volume: 64
issue: 12
More Information: 2988,
2996
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8049487.pdf672.92 kBAdobe PDF
Title: Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models
Authors: Vishal Vijayakumar;Michelle Case;Sina Shirinpour;Bin He
Year: 2017
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/226107
volume: 64
issue: 12
More Information: 2988,
2996
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8049487.pdf672.92 kBAdobe PDF
Title: Quantifying and Characterizing Tonic Thermal Pain Across Subjects From EEG Data Using Random Forest Models
Authors: Vishal Vijayakumar;Michelle Case;Sina Shirinpour;Bin He
Year: 2017
Publisher: IEEE
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.
URI: http://localhost/handle/Hannan/226107
volume: 64
issue: 12
More Information: 2988,
2996
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8049487.pdf672.92 kBAdobe PDF