Detection of acute tonic cold pain from microwave transcranial transmission signals obtained via the microwave scattering approach

Abstract

To investigate the feasibility of detecting acute tonic cold pain (CP) perception from recordable microwave transcranial transmission (MTT) signals by using machine learning techniques. CP and no-pain (NP) MTT signals collected from 15 young subjects are analyzed in the wavelet packet transformation (WPT) and variational mode decomposition (VMD) domains. In addition, features such as relative energy change, refined composite multiscale dispersion entropy, refined composite multiscale fuzzy entropy, and autoregressive model coefficients are extracted in the WPD, VMD, VMD-WPD, and WPD-VMD domains. Simultaneously, support vector machine (SVM) is selected as the classifier, and feature indexes are input into the classifier by using the 10-fold cross validation method to obtain the best training and test datasets. Principal component analysis is used to reduce the feature dimensions of the training and test datasets and to improve classification accuracy. Then, the test dataset is imported into the trained classifier for the calculation and evaluation of the model's classification performance. In the validation of the SVM classifier, feature extraction in the WPD-VMD domain is the best pain detection algorithm. It provides high values of sensitivity (91.30%), specificity (90.47%), positive predictive value (91.30%), accuracy (90.90%), and area under curve (0.806). The microwave scattering technique can be used as a direct, objective, and experimentally stable method to detect acute CP perception, this approach has a high application prospect for clinical real-time diagnosis.