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A method of REM-NREM sleep distinction using ECG signal for unobtrusive personal monitoring

机译：一种使用ECG信号进行不干扰个人监测的REM-NREM睡眠区分方法

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Computers are used extensively in sleep labs for polysomnography and for assistance in sleep staging. However, the test is highly inconvenient to the patient and requires availability of specially equipped sleep labs. Alternative approaches that enable unobtrusive in-home sleep staging with ECG or other signals are highly desirable. In this paper we describe a method that can be used for distinction of REM and NREM sleep stages using spectral and non-linear features of ECG derived RR interval series. To test the accuracy of the system, we extracted the RR interval series from sleep studies of 20 young healthy individuals. Time domain, spectral and non-linear features were computed and tested for discriminability. Features showing high degree of discrimination were selected. A polynomial support vector machine was trained with selected features - percent power in HF band, LF/HF, Poincare plot parameters, exponents from Detrended fluctuation analysis, and sampEn of the half of the signals. The hyperplane was used to classify the other half of the data. The results show an accuracy of 76.25% with Cohen's kappa as 0.52 for a two-class model of five minute signal. The results dropped to 72.8% accuracy and k=0.48 for the two class model of one minute signal. The minimal set offers a reasonable trade-off for possible in-home monitoring, at least for some conditions that require only REM-NREM distinction. The method after extensive trials and standardisation, can alleviate the load of special purpose PSG labs and enable the tests to be done on general purpose computers.
机译：睡眠实验室广泛使用计算机进行多导睡眠监测和协助进行睡眠分期。但是，该测试给患者带来了极大的不便，并且需要配备专门配备的睡眠实验室。非常需要能够使用ECG或其他信号进行在家中不分阶段睡眠的替代方法。在本文中，我们描述了一种方法，该方法可使用ECG派生的RR间隔序列的频谱和非线性特征来区分REM和NREM睡眠阶段。为了测试系统的准确性，我们从20位年轻健康个体的睡眠研究中提取了RR间隔序列。计算时域，频谱和非线性特征，并测试其可分辨性。选择显示出高度区分度的特征。对多项式支持向量机进行了训练，使其具有选定的功能-HF频带中的功率百分比，LF / HF，Poincare图参数，去趋势波动分析的指数以及一半信号的sampEn。超平面用于对数据的另一半进行分类。结果表明，对于五分钟信号的两类模型，Cohen的kappa为0.52时，准确度为76.25％。对于一分钟信号的两类模型，结果下降到72.8％的精度，k = 0.48。最小设置为可能的家庭监视提供了合理的权衡，至少在某些仅需要REM-NREM区分的条件下。经过广泛试验和标准化的方法，可以减轻专用PSG实验室的负担，并使测试可以在通用计算机上进行。

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