Geng Daoshuang, Yang Daoguo, Cai Miao, Zheng Lixia
School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
College of Continuing Education, Guilin University of Electronic Technology, Guilin 541004, China.
Entropy (Basel). 2020 Mar 17;22(3):347. doi: 10.3390/e22030347.
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.
本研究的目的是开发一种用于健康监测的非接触式睡眠阶段检测与睡眠障碍治疗集成系统。因此，开发了一种基于微波散射技术而非头皮脑电图的脑活动检测方法来评估睡眠阶段。首先，使用特定频率的微波穿透睡眠障碍患者大脑的功能部位，以改变大脑激活区域的放电频率，并对改善睡眠的效果进行统计分析和评估。然后，采用小波包算法对微波传输信号进行分解，从小波包系数中提取精细复合多尺度样本熵、基于精细复合多尺度波动的离散熵和多变量多尺度加权排列熵作为特征。最后，使用互信息-主成分分析特征选择方法优化特征集，并使用随机森林对睡眠阶段进行分类和评估。结果表明，经过四次微波调制治疗后，睡眠效率持续提高，总体维持在80%以上，失眠率逐渐降低。四个睡眠阶段的总体分类准确率为86.4%。结果表明，特定频率的微波可以治疗睡眠障碍并检测异常脑活动。因此，微波散射方法在新型脑病治疗、诊断和临床应用系统的开发中具有重要意义。
