Addison Paul S, Jacquel Dominique, Foo David M H, Antunes André, Borg Ulf R
From the *Video Biosignals Group, Medtronic Respiratory & Monitoring Solutions, Technopole Centre, Edinburgh, United Kingdom; and †Medical Affairs, Medtronic Respiratory & Monitoring Solutions, Boulder, Colorado.
Anesth Analg. 2017 Sep;125(3):860-873. doi: 10.1213/ANE.0000000000001989.
The physiologic information contained in the video photoplethysmogram is well documented. However, extracting this information during challenging conditions requires new analysis techniques to capture and process the video image streams to extract clinically useful physiologic parameters. We hypothesized that heart rate, respiratory rate, and oxygen saturation trending can be evaluated accurately from video information during acute hypoxia.
Video footage was acquired from multiple desaturation episodes during a porcine model of acute hypoxia using a standard visible light camera. A novel in-house algorithm was used to extract photoplethysmographic cardiac pulse and respiratory information from the video image streams and process it to extract a continuously reported video-based heart rate (HRvid), respiratory rate (RRvid), and oxygen saturation (SvidO2). This information was then compared with HR and oxygen saturation references from commercial pulse oximetry and the known rate of respiration from the ventilator.
Eighty-eight minutes of data were acquired during 16 hypoxic episodes in 8 animals. A linear mixed-effects regression showed excellent responses relative to a nonhypoxic reference signal with slopes of 0.976 (95% confidence interval [CI], 0.973-0.979) for HRvid; 1.135 (95% CI, 1.101-1.168) for RRvid, and 0.913 (95% CI, 0.905-0.920) for video-based oxygen saturation. These results were obtained while maintaining continuous uninterrupted vital sign monitoring for the entire study period.
Video-based monitoring of HR, RR, and oxygen saturation may be performed with reasonable accuracy during acute hypoxic conditions in an anesthetized porcine hypoxia model using standard visible light camera equipment. However, the study was conducted during relatively low motion. A better understanding of the effect of motion and the effect of ambient light on the video photoplethysmogram may help refine this monitoring technology for use in the clinical environment.
视频光电容积脉搏波图中包含的生理信息已有充分记录。然而，在具有挑战性的条件下提取这些信息需要新的分析技术来捕获和处理视频图像流，以提取临床上有用的生理参数。我们假设在急性缺氧期间，可以从视频信息中准确评估心率、呼吸频率和血氧饱和度趋势。
使用标准可见光相机，从猪急性缺氧模型的多次去饱和发作中获取视频片段。一种新颖的内部算法用于从视频图像流中提取光电容积脉搏波心脏搏动和呼吸信息，并对其进行处理，以提取连续报告的基于视频的心率（HRvid）、呼吸频率（RRvid）和血氧饱和度（SvidO2）。然后将该信息与商业脉搏血氧仪的心率和血氧饱和度参考值以及呼吸机已知的呼吸频率进行比较。
在8只动物的16次缺氧发作期间获取了88分钟的数据。线性混合效应回归显示，相对于非缺氧参考信号，HRvid的斜率为0.976（95%置信区间[CI]，0.973 - 0.979）；RRvid的斜率为1.135（95%CI，1.101 - 1.168）；基于视频的血氧饱和度的斜率为0.913（95%CI，0.905 - 0.920），反应良好。这些结果是在整个研究期间保持连续不间断的生命体征监测的情况下获得的。
在麻醉的猪缺氧模型中，使用标准可见光相机设备，在急性缺氧条件下基于视频监测心率、呼吸频率和血氧饱和度可能具有合理的准确性。然而，该研究是在相对低运动状态下进行的。更好地了解运动和环境光对视频光电容积脉搏波图的影响可能有助于改进这种监测技术以用于临床环境。
