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.

This study aims to improve the accuracy of detecting acute tonic cold pain (CP) perception from microwave transcranial transmission (MTT) signals. Two different types of CP and no-pain (NP) MTT signals are obtained from 15 subjects. Four features, namely, power spectral exponential entropy, improved multiscale permutation entropy, refined composite multiscale dispersion entropy, and refined composite multiscale fuzzy entropy, are extracted in the variational modal decomposition domain. The feature datasets are divided into training datasets and test datasets in a 3:1 ratio. Random forest (RF) and support vector machine (SVM) are selected as classifiers. The training datasets are imported into the classifier, and the optimal training dataset is obtained with a 10-fold cross validation strategy. The feature dimension reduction algorithm of the principal component analysis is used to reduce the complexity of the feature datasets and select the most recognizable features. The classification performance of the test datasets is evaluated by the optimal classifiers. Results showed that the RF classifier performs better than the SVM classifier. The RF classifier provides high values of specificity (91.67%), sensitivity (95.83%), positive predictive value (92.00%), accuracy (93.75%), and area under curve (0.867). The combination of the microwave detection approach and machine learning algorithm can effectively detect brain activity induced by nociceptive stimulation. This approach is important in improving the accuracy of pain detection.

The aim of this study was to develop an integrated system of non-contact sleep stage detection and sleep disorder treatment for health monitoring. Hence, a method of brain activity detection based on microwave scattering technology instead of scalp electroencephalogram was developed to evaluate the sleep stage. First, microwaves at a specific frequency were used to penetrate the functional sites of the brain in patients with sleep disorders to change the firing frequency of the activated areas of the brain and analyze and evaluate statistically the effects on sleep improvement. Then, a wavelet packet algorithm was used to decompose the microwave transmission signal, the refined composite multiscale sample entropy, the refined composite multiscale fluctuation-based dispersion entropy and multivariate multiscale weighted permutation entropy were obtained as features from the wavelet packet coefficient. Finally, the mutual information-principal component analysis feature selection method was used to optimize the feature set and random forest was used to classify and evaluate the sleep stage. The results show that after four times of microwave modulation treatment, sleep efficiency improved continuously, the overall maintenance was above 80%, and the insomnia rate was reduced gradually. The overall classification accuracy of the four sleep stages was 86.4%. The results indicate that the microwaves with a certain frequency can treat sleep disorders and detect abnormal brain activity. Therefore, the microwave scattering method is of great significance in the development of a new brain disease treatment, diagnosis and clinical application system.

Understanding the defect characterization of electronic and mechanical components is a crucial step in diagnosing component lifetime. Technologies for determining reliability, such as thermal modeling, cohesion modeling, statistical distribution, and entropy generation analysis, have been developed widely. Defect analysis based on the irreversibility entropy generation methodology is favorable for electronic and mechanical components because the second law of thermodynamics plays a unique role in the analysis of various damage assessment problems encountered in the engineering field. In recent years, numerical and theoretical studies involving entropy generation methodologies have been carried out to predict and diagnose the lifetime of electronic and mechanical components. This work aimed to review previous defect analysis studies that used entropy generation methodologies for electronic and mechanical components. The methodologies are classified into two categories, namely, damage analysis for electronic devices and defect diagnosis for mechanical components. Entropy generation formulations are also divided into two detailed derivations and are summarized and discussed by combining their applications. This work is expected to clarify the relationship among entropy generation methodologies, and benefit the research and development of reliable engineering components.

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