Automatic sleep stages classification using respiratory, heart rate and movement signals
Automatic sleep stages classification using respiratory, heart rate and movement signals
复制标题
DOI：
10.1088/1361-6579/aaf5d4
复制
发表时间：
2018-12-01
影响因子：
3.2
通讯作者：
Seepold, Ralf
中科院分区：
文献类型：
--
作者：
Gaiduk, Maksym;Penzel, Thomas;Seepold, Ralf
目的：提出一种基于呼吸、心率和运动信号的无创睡眠阶段识别算法。该算法是一个适合在家庭环境中长期监测的系统的一部分，它应该支持专家分析睡眠。方法：由于生物生命信号与睡眠阶段有很强的相关性，因此选择多项逻辑回归进行睡眠阶段的分类分布。该方法输入了三个信号（呼吸、心率和运动）的几个衍生参数。研究人员将5名受试者的睡眠记录用于机器学习模型的训练，并对30名受试者的30个夜间记录进行评估，这些记录大约有27000个epoch，每个epoch间隔30秒。主要结果：Wake、NREM、REM阶段的准确率为72% (Cohen’s kappa值为0.67),Wake、Light （N1和N2）、Deep （N3）和REM阶段的准确率为58% （Cohen’s kappa值为0.50）。我们的方法证实了这种方法的潜力，并揭示了几种改进方法。意义：研究结果表明，呼吸、心率和运动信号可用于睡眠研究，具有合理的准确性。这些输入可以以一种非侵入性的方式在家庭环境中应用。该系统为支持睡眠实验室的长期监测系统引入了一种方便的方法。所开发的算法允许根据可用信号轻松调整输入参数，因此也可以与各种硬件系统一起使用。
Objective: This paper presents an algorithm for non-invasive sleep stage identification using respiratory, heart rate and movement signals. The algorithm is part of a system suitable for longterm monitoring in a home environment, which should support experts analysing sleep. Approach: As there is a strong correlation between bio-vital signals and sleep stages, multinomial logistic regression was chosen for categorical distribution of sleep stages. Several derived parameters of three signals (respiratory, heart rate and movement) are input for the proposed method. Sleep recordings of five subjects were used for the training of a machine learning model and 30 overnight recordings collected from 30 individuals with about 27 000 epochs of 30 s intervals each were evaluated. Main results: The achieved rate of accuracy is 72% for Wake, NREM, REM (with Cohen's kappa value 0.67) and 58% for Wake, Light (N1 and N2), Deep (N3) and REM stages (Cohen's kappa is 0.50). Our approach has confirmed the potential of this method and disclosed several ways for its improvement. Significance: The results indicate that respiratory, heart rate and movement signals can be used for sleep studies with a reasonable level of accuracy. These inputs can be obtained in a non-invasive way applying it in a home environment. The proposed system introduces a convenient approach for a long-term monitoring system which could support sleep laboratories. The algorithm which was developed allows for an easy adjustment of input parameters that depend on available signals and for this reason could also be used with various hardware systems.