Milerska Iva, Kremen Vaclav, Gerla Vaclav, St Louis Erik K, Lhotska Lenka
Faculty of Electrical Engineering, Czech Technical University, Department of Cybernetics, Prague, Czech Republic.
The Czech Istitute of Informatics, Robotics and Cybernetics, Czech Technival University, Prague, Czech Republic.
Biomed Signal Process Control. 2019 May;51:243-252. doi: 10.1016/j.bspc.2019.02.023. Epub 2019 Mar 7.
We aimed at evaluating semi-automatic detection and quantification of polysomnographic REM sleep without atonia (RSWA). As basic requirements, we defined lower time demand, the possibility of comparison of several evaluations and ease of examination for neurologists. We focused on well-known primary processing of surface electromyographic signals and selected recordings that were free of technical artifacts that could compromise automated signal detection. Thus we created a comprehensive method consisting of several modules (data preprocessing, signal filtration, envelopes creation, detection of ECG QRS complexes, iterative RSWA detection, detection evaluation and interactive visualization). The original dataset consisted of 7 individual polysomnography (PSG) recordings of individual human adult subjects with REM sleep behavior disorder (RBD). RSWA detection was performed with three different methods for envelope creation (envelope by moving average filter, envelope by Savitzky-Golay filtration and peaks interpolation). Best RSWA detection was achieved using the envelope by moving average filter (average precision 64.24±12.34 % and recall 91.63±10.07 %). The lowest precision was 42.86 % with 100 % recall.
我们旨在评估多导睡眠图快速眼动睡眠无张力缺失（RSWA）的半自动检测和量化。作为基本要求，我们定义了较低的时间需求、多次评估结果可比较的可能性以及便于神经科医生进行检查。我们专注于表面肌电信号的著名初级处理，并选择了无可能影响自动信号检测的技术伪迹的记录。因此，我们创建了一种由多个模块组成的综合方法（数据预处理、信号滤波、包络创建、心电图QRS复合波检测、迭代RSWA检测、检测评估和交互式可视化）。原始数据集由7份患有快速眼动睡眠行为障碍（RBD）的成年个体的多导睡眠图（PSG）记录组成。使用三种不同的包络创建方法（移动平均滤波器包络、Savitzky-Golay滤波包络和峰值插值）进行RSWA检测。使用移动平均滤波器包络实现了最佳的RSWA检测（平均精度64.24±12.34%，召回率91.63±10.07%）。最低精度为42.86%，召回率为100%。
