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

• Published in: Frontiers in bioscience (Landmark. Print) Vol. 26; no. 12; pp. 1537 - 1547
• Main Authors: Hsiao, Fu-Jung, Chen, Wei-Ta, Pan, Li-Ling Hope, Liu, Hung-Yu, Wang, Yen-Feng, Chen, Shih-Pin, Lai, Kuan-Lin, Wang, Shuu-Jiun
• Format: Journal Article
• Language: English
• Published: Singapore IMR Press 30.12.2021
• Subjects: Algorithms; Electroencephalography; functional connectivity; Hot Temperature; Humans; Machine Learning; Pain - diagnosis; pain sensitivity; power spectral density; resting-state eeg; Support Vector Machine; Functional connectivity; Pain sensitivity; Power spectral density; Resting-state EEG; Support vector machine; Machine learning
• ISSN: 2768-6701; 2768-6698
• DOI: 10.52586/5047

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. 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. 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. 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.