Reyes Bersain A, Reljin Natasa, Kong Youngsun, Nam Yunyoung, Chon Ki H
IEEE J Biomed Health Inform. 2017 May;21(3):764-777. doi: 10.1109/JBHI.2016.2532876. Epub 2016 Feb 25.
Two parameters that a breathing status monitor should provide include tidal volume ( V) and respiration rate (RR). Recently, we implemented an optical monitoring approach that tracks chest wall movements directly on a smartphone. In this paper, we explore the use of such noncontact optical monitoring to obtain a volumetric surrogate signal, via analysis of intensity changes in the video channels caused by the chest wall movements during breathing, in order to provide not only average RR but also information about V and to track RR at each time instant (IRR). The algorithm, implemented on an Android smartphone, is used to analyze the video information from the smartphone's camera and provide in real time the chest movement signal from N = 15 healthy volunteers, each breathing at V ranging from 300 mL to 3 L. These measurements are performed separately for each volunteer. Simultaneous recording of volume signals from a spirometer is regarded as reference. A highly linear relationship between peak-to-peak amplitude of the smartphone-acquired chest movement signal and spirometer V is found ( r = 0.951 ±0.042, mean ± SD). After calibration on a subject-by-subject basis, no statistically significant bias is found in terms of V estimation; the 95% limits of agreement are -0.348 to 0.376 L, and the root-mean-square error (RMSE) was 0.182 ±0.107 L. In terms of IRR estimation, a highly linear relation between smartphone estimates and the spirometer reference was found ( r = 0.999 ±0.002). The bias, 95% limits of agreement, and RMSE are -0.024 breaths-per-minute (bpm), -0.850 to 0.802 bpm, and 0.414 ±0.178 bpm, respectively. These promising results show the feasibility of developing an inexpensive and portable breathing monitor, which could provide information about IRR as well as V, when calibrated on an individual basis, using smartphones. Further studies are required to enable practical implementation of the proposed approach.
呼吸状态监测仪应提供的两个参数包括潮气量（V）和呼吸频率（RR）。最近，我们实施了一种光学监测方法，可直接在智能手机上跟踪胸壁运动。在本文中，我们探索使用这种非接触式光学监测通过分析呼吸期间胸壁运动引起的视频通道强度变化来获取体积替代信号，以便不仅提供平均RR，还提供有关V的信息，并在每个时刻跟踪RR（即时RR，IRR）。在安卓智能手机上实现的该算法用于分析来自智能手机摄像头的视频信息，并实时提供来自15名健康志愿者的胸部运动信号，每名志愿者的呼吸潮气量范围为300毫升至3升。这些测量针对每名志愿者分别进行。将同时记录的来自肺活量计的体积信号视为参考。发现智能手机获取的胸部运动信号的峰峰值幅度与肺活量计V之间存在高度线性关系（r = 0.951±0.042，均值±标准差）。在逐个受试者进行校准后，在V估计方面未发现统计学上的显著偏差；一致性界限的95%为-0.348至0.376升，均方根误差（RMSE）为0.182±0.107升。在IRR估计方面，发现智能手机估计值与肺活量计参考值之间存在高度线性关系（r = 0.999±0.002）。偏差、一致性界限的95%和RMSE分别为-0.024次呼吸每分钟（bpm）、-0.850至0.802 bpm和0.414±0.178 bpm。这些有前景的结果表明开发一种廉价且便携的呼吸监测仪是可行的，当使用智能手机进行个体校准时，该监测仪可以提供有关IRR以及V的信息。需要进一步研究以实现所提出方法的实际应用。
