Machine learning-based prediction of heat pain sensitivity by using resting-state EEG

Fu-Jung Hsiao; Wei-Ta Chen; Li-Ling Hope Pan; Hung-Yu Liu; Yen-Feng Wang; Shih-Pin Chen; Kuan-Lin Lai; Shuu-Jiun Wang

doi:10.52586/5047

Machine learning-based prediction of heat pain sensitivity by using resting-state EEG

Front Biosci (Landmark Ed). 2021 Dec 30;26(12):1537-1547. doi: 10.52586/5047.

Authors

Fu-Jung Hsiao¹, Wei-Ta Chen^{1

2

3

4}, Li-Ling Hope Pan¹, Hung-Yu Liu^{2

3}, Yen-Feng Wang^{2

3}, Shih-Pin Chen^{1

2

3}, Kuan-Lin Lai^{2

3}, Shuu-Jiun Wang^{1

2

3}

Affiliations

¹ Brain Research Center, National Yang-Ming Chiao-Tung University, 11221 Taipei, Taiwan.
² School of Medicine, National Yang-Ming Chiao-Tung University, 11221 Taipei, Taiwan.
³ Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, 11217 Taipei, Taiwan.
⁴ Department of Neurology, Keelung Hospital, Ministry of Health and Welfare, 20147 Keelung, Taiwan.

PMID: 34994168
DOI: 10.52586/5047

Abstract

Introduction: The development of quantitative, objective signatures or predictors to evaluate pain sensitivity is crucial in the clinical management of pain and in precision medicine. This study combined multimodal (neurophysiology and psychometrics) signatures to classify the training dataset and predict the testing dataset on individual heat pain sensitivity.

Methods: Healthy individuals were recruited in this study. Individual heat pain sensitivity and psychometric scores, as well as the resting-state electroencephalography (EEG) data, were obtained from each participant. Participants were divided into low-sensitivity and high-sensitivity subgroups according to their heat pain sensitivity. Psychometric data obtained from psychometric measurements and power spectral density (PSD) and functional connectivity (FC) derived from resting-state EEG analysis were subjected to feature selection with an independent t test and were then trained and predicted using machine learning models, including support vector machine (SVM) and k-nearest neighbor.

Results: In total, 85 participants were recruited in this study, and their data were divided into training (n = 65) and testing (n = 20) datasets. We identified the resting-state PSD and FC, which can serve as brain signatures to classify heat pain as high-sensitive or low-sensitive. Using machine learning algorithms of SVM with different kernels, we obtained an accuracy of 86.2%-93.8% in classifying the participants into thermal pain high-sensitivity and low-sensitivity groups; moreover, using the trained model of cubic SVM, an accuracy of 80% was achieved in predicting the pain sensitivity of an independent dataset of combined PSD and FC features of resting-state EEG data.

Conclusion: Acceptable accuracy in classification and prediction by using the SVM model indicated that pain sensitivity could be achieved, leading to considerable possibilities of the use of objective evaluation of pain perception in clinical practice. However, the predictive model presented in this study requires further validation by studies with a larger dataset.

Keywords: Functional connectivity; Machine learning; Pain sensitivity; Power spectral density; Resting-state EEG; Support vector machine.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Electroencephalography*
Hot Temperature*
Humans
Machine Learning
Pain / diagnosis
Support Vector Machine