Extraction of breathing features using MS Kinect for sleep stage detection
Extraction of breathing features using MS Kinect for sleep stage detection
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DOI：
10.1007/s11760-016-0897-2
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发表时间：
2016-10-01
影响因子：
2.3
通讯作者：
Valis, Martin
中科院分区：
文献类型：
--
作者：
Prochazka, Ales;Schatz, Martin;Valis, Martin
本文介绍了使用MS Kinect深度传感器的非接触式呼吸测量，并将测量结果与多导睡眠描记仪（PSG）的呼吸记录进行了比较。我们探索了信号去噪、重采样和频谱分析的方法，以及特征提取和贝叶斯分类。所提出的方法被应用于分析睡眠实验室中PSG和MS Kinect同时观察的个体的长期监测。将多导睡眠图和MS Kinect视频数据进行时间同步后，提取两种信号的特征并进行比较。MS Kinect评估频率时与PSG相关的平均误差为3.75%。在PSG和MS Kinect测量中，贝叶斯将特征分为两类（即清醒或睡眠）的平均准确率分别为88.90%和88.95%。特征的强烈相似性支持了非接触式技术可能代表当前睡眠监测方法的有效替代方法的假设，从而允许在家庭环境中获取数据。
This paper presents the contactless measuring of breathing using the MS Kinect depth sensor and compares the results obtained with records of breathing taken by polysomnography (PSG). We explore the methods of signal denoising, resampling, and spectral analysis of acquired data as well as feature extraction and their Bayesian classification. The proposed methodology was applied for analysis of the long-term monitoring of individuals who were observed simultaneously by PSG and MS Kinect in the sleep laboratory. After time synchronization of polysomnographic and MS Kinect video data, features were extracted from both signals and compared. The average error of the frequency while being evaluated by MS Kinect that was related to that obtained by PSG was 3.75 %. The mean accuracy of the Bayesian classification of features into two classes (i.e. wake or sleep) was 88.90 and 88.95 % for the PSG and MS Kinect measurements, respectively. The strong likeness of features supports the hypothesis that contactless techniques may represent a valid alternative to the present approach of sleep monitoring, thereby allowing data acquisition in the home environment as well.