急性缺氧反应期间视频生理监测：心率，呼吸频率和氧饱和度
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Video-Based Physiologic Monitoring During an Acute Hypoxic Challenge: Heart Rate, Respiratory Rate, and Oxygen Saturation
背景与目的
视频血管容积图包含的生理信息可以被很好的记录。在急性缺氧反应条件下提取信息需要新的分析技术，并且需要处理图像信息数据才能提取有价值的临床生理参数。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，呼吸频率和氧饱和度趋势。
方 法
在使用标准可见光相机设备的猪麻醉缺氧模型中，急性缺氧条件下视频监测的HR，RR和氧饱和度尚属准确。然而，研究是在相对较低的运动期间进行的。了解更多的运动和环境光对视频光电容积图的影响可能有助于将此监测技术应用于临床。
结 果
在8只动物的16次缺氧反应中获得了88分钟的数据。混和线性回归显示相对于具有0.976（95％心率间隔[CI]，0.973-0.979））的非低氧参考斜率信号的HRVid的反应优异; 对于RRvid，1.135（95％CI，1.101-1.168），可视氧饱和度的0.913（95％CI，0.905-0.920）反应优异。这些结果是在整个研究期间持续的生命体征监测中获得
结 论
在使用标准可见光相机设备的猪麻醉缺氧模型中，急性缺氧条件下视频监测的HR，RR和氧饱和度尚属准确。然而，研究是在相对较低的运动期间进行的。了解更多的运动和环境光对视频光电容积图的影响可能有助于将此监测技术应用于临床
原始文献摘要
Addison P S, Jacquel D, Foo D M, et al. Video-Based Physiologic Monitoring During an Acute Hypoxic Challenge: Heart Rate, Respiratory Rate, and Oxygen Saturation[J]. Anesthesia & Analgesia, 2017, 125.
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
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