University of Chinese Academy of Sciences, Beijing 101408, China.
CAS Institute of Healthcare Technologies, Nanjing 210046, China.
Sensors (Basel). 2022 Mar 14;22(6):2225. doi: 10.3390/s22062225.
The cyclic alternating pattern is the periodic electroencephalogram activity occurring during non-rapid eye movement sleep. It is a marker of sleep instability and is correlated with several sleep-related pathologies. Considering the connection between the human heart and brain, our study explores the feasibility of using cardiopulmonary features to automatically detect the cyclic alternating pattern of sleep and hence diagnose sleep-related pathologies. By statistically analyzing and comparing the cardiopulmonary characteristics of a healthy group and groups with sleep-related diseases, an automatic recognition scheme of the cyclic alternating pattern is proposed based on the cardiopulmonary resonance indices. Using the Hidden Markov and Random Forest, the scheme combines the variation and stability of measurements of the coupling state of the cardiopulmonary system during sleep. In this research, the F1 score of the sleep-wake classification reaches 92.0%. In terms of the cyclic alternating pattern, the average recognition rate of A-phase reaches 84.7% on the CAP Sleep Database of 108 cases of people. The F1 score of disease diagnosis is 87.8% for insomnia and 90.0% for narcolepsy.
周期性交替模式是发生在非快速眼动睡眠期间的脑电图周期性活动。它是睡眠不稳定的标志，与几种与睡眠相关的疾病有关。鉴于人心与脑之间的联系，我们的研究探索了使用心肺特征自动检测睡眠周期性交替模式并因此诊断与睡眠相关的疾病的可行性。通过对健康组和具有睡眠相关疾病的组的心肺特征进行统计分析和比较，提出了一种基于心肺共振指数的周期性交替模式自动识别方案。使用隐马尔可夫模型和随机森林，该方案结合了睡眠期间心肺系统耦合状态测量的变化和稳定性。在这项研究中，睡眠-觉醒分类的 F1 分数达到 92.0%。就周期性交替模式而言，在 108 例 CAP 睡眠数据库中，A 阶段的平均识别率达到 84.7%。失眠症的疾病诊断 F1 得分为 87.8%，发作性睡病的 F1 得分为 90.0%。
