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Evaluation of Acute Tonic Cold Pain From Microwave Transcranial Transmission Signals Using Multi-Entropy Machine Learning Approach

机译：使用多熵机学习方法评估微波经变频信号的急性滋补冷疼痛

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摘要

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.
机译：本研究旨在提高检测急性滋补冷疼痛（CP）从微波经颅透射（MTT）信号的敏感性的准确性。从15个受试者获得两种不同类型的CP和无疼痛（NP）MTT信号。在变分模式分解域中提取了四个特征，即功率谱指数熵，改进的多尺寸置位熵，精制复合材料多尺寸模糊熵和精制复合多尺寸模糊熵。特征数据集分为训练数据集和测试数据集3：1的比例。随机森林（RF）和支持向量机（SVM）被选为分类器。训练数据集导入分类器，并且使用10倍的交叉验证策略获得最佳训练数据集。主要成分分析的特征尺寸减少算法用于降低特征数据集的复杂性，并选择最识别最识别的功能。测试数据集的分类性能由最佳分类器评估。结果表明，RF分类器比SVM分类器更好。 RF分类器提供高特异性值（91.67％），灵敏度（95.83％），阳性预测值（92.00％），精度（93.75％）和曲线区域（0.867）。微波检测方法和机器学习算法的组合可以有效地检测伤害刺激诱导的脑活动。这种方法对于提高疼痛检测的准确性是重要的。

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