| Computer Technology, Electronic Communications Technologies |
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| Sleep staging methods using pulse frequency variability |
| NI Hong-bo, DENG Jun-quan, SHI Xiang-nan, ZHOU Xing-she,ZHAO Wei-chao, SONG Ya-long, JIA Jiang-bo |
| School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China |
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Abstract A novel sleep stage classification method using pulse rate variability analysis was proposed in order to learn one’s sleep pattern in home environment. First, blood oxygen fingerstall was employed to obtain the original pulse data; the data was processed with multi-distinguishability wavelet transformation method; the first layer of the signal was extracted to obtain the pulse interval with a self-adapted sliding window algorithm. Next, the pulse rate variability features were extracted from the pulse interval information acquired before. Finally, the sleep stage could be classified according to the differences between various stages of sleep. During the classification process, time domain, frequency domain as well as nonlinear domain of the pulse rate variability signals were main references. After all the features being extracted from the data, the features were estimated and the sleep staging classification was given out. The experiment results were evaluated and analyzed. In this experiment environment, the precision of sleep stage classification can reach 76%, indicating the effectiveness of our method, which also means that the method can classify one’s sleep stage efficiently in home environment with low cost．
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Published: 01 March 2017
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Cite this article:
NI Hong-bo, DENG Jun-quan, SHI Xiang-nan, ZHOU Xing-she,ZHAO Wei-chao, SONG Ya-long, JIA Jiang-bo. Sleep staging methods using pulse frequency variability. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(3): 572-576.
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脉率变异性睡眠分期方法
为了能让用户在居家环境中不影响睡眠质量的条件下了解自己的睡眠状况,提出一种基于脉率变异性的睡眠分期方法.使用血氧指套获得用户在睡眠状况下的脉搏波,进行小波分解并采用其中的第一层信号;使用自适应的滑动窗口提取单个脉搏间期,得到脉搏间期序列之后据此对睡眠阶段进行划分.在划分过程中,首先对提取出的脉搏间期信号的时域、频域和非线性域特征进行提取；再进行特征评价,训练出一组分类器对睡眠进行分期；最后对得出的结果进行分析.实验结果证明,在该实验环境中的睡眠分期准确率可达到76%,说明该方法在家庭环境下可实现低成本、高效性的睡眠分期.
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