摘要翻译：
我们开发并评估了一种呼吸频率估计算法，该算法利用压敏垫(PSM)技术的数据，用于新生儿重症监护病房(NICU)的连续病人监测。分析了PSM数据中漂移的随机效应和蠕变的系统效应，表明这些效应本质上依赖于外加载荷和接触面。不确定度测量是评估生理参数的关键。PSM数据中的标准不确定度在这里用百分比漂移表示。接下来，我们评估PSM技术在新生儿患者模拟试验中估计RR的适用性，包括内外诱导运动、床垫类型、咕噜声、放置位置和不同呼吸频率等五种混合效应。我们分析了混合效应模型的一致性极限，以导出通过两种估计技术获得的估计RR中的不确定性。与金标准RR值相比，我们获得了每分钟0.56次呼吸(bpm)的平均偏差，误差在[-2.26,3.37]bpm的95%可信区间内。这些结果在+/-5 bpm内满足RR的临床准确性要求。
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英文标题：
《Measuring uncertainty during respiratory rate estimation using
  pressure-sensitive mats》
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作者：
S. Nizami, A. Bekele, M. Hozayen, K. Greenwood, J. Harrold and J. R.
  Green
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  We develop and evaluate a respiratory rate estimation algorithm that utilizes data from pressure-sensitive mat (PSM) technology for continuous patient monitoring in neonatal intensive care units (NICU). An analysis of the random effect of drift and systematic effect of creep in the PSM data is presented, showing that these are essentially dependent on the applied load and contact surface. Uncertainty measurements are pivotal when estimating physiologic parameters. The standard uncertainty in the PSM data is here represented by the percent drift. Next, we evaluate the applicability of PSM technology to estimate RR in neonatal patient simulator trials under five mixed effects including internally and externally induced motion, mattress type, grunting, laying position, and different breathing rates. We analyze the limits of agreement on the mixed effects model to derive the uncertainty in the estimated RR obtained through two estimation techniques. In comparison with the gold standard RR values, we achieved a mean bias of 0.56 breaths per minute (bpm) with an error bounded by a 95% confidence interval of [-2.26, 3.37] bpm. These results meet the clinical accuracy requirements of RR within +/-5 bpm.
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PDF链接：
https://arxiv.org/pdf/1805.00082