Institute of Biomedical Engineering, TU Dresden, Fetscherstraße 29, 01307, Dresden, Germany.
Department of Anesthesiology and Intensive Care Medicine, University Hospital, TU Dresden, Fetscherstraße 74, 01307, Dresden, Germany.
Biomed Eng Online. 2018 Mar 14;17(1):33. doi: 10.1186/s12938-018-0467-7.
Camera-based photoplethysmography (cbPPG) is a measurement technique which enables remote vital sign monitoring by using cameras. To obtain valid plethysmograms, proper regions of interest (ROIs) have to be selected in the video data. Most automated selection methods rely on specific spatial or temporal features limiting a broader application. In this work, we present a new method which overcomes those drawbacks and, therefore, allows cbPPG to be applied in an intraoperative environment.
We recorded 41 patients during surgery using an RGB and a near-infrared (NIR) camera. A Bayesian skin classifier was employed to detect suitable regions, and a level set segmentation approach to define and track ROIs based on spatial homogeneity.
The results show stable and homogeneously illuminated ROIs. We further evaluated their quality with regards to extracted cbPPG signals. The green channel provided the best results where heart rates could be correctly estimated in 95.6% of cases. The NIR channel yielded the highest contribution in compensating false estimations.
The proposed method proved that cbPPG is applicable in intraoperative environments. It can be easily transferred to other settings regardless of which body site is considered.
基于摄像的光体积描记法（cbPPG）是一种通过摄像头实现远程生命体征监测的测量技术。为了获得有效的容积脉搏图，必须在视频数据中选择适当的感兴趣区域（ROI）。大多数自动化选择方法都依赖于特定的空间或时间特征，限制了更广泛的应用。在这项工作中，我们提出了一种新的方法，克服了这些缺点，因此允许 cbPPG 在手术室内环境中应用。
我们使用 RGB 和近红外（NIR）相机记录了 41 名手术患者的数据。采用贝叶斯皮肤分类器检测合适的区域，并采用水平集分割方法基于空间同质性定义和跟踪 ROI。
结果显示稳定且均匀照明的 ROI。我们进一步评估了它们在提取 cbPPG 信号方面的质量。绿色通道提供了最佳结果，其中 95.6%的情况下可以正确估计心率。NIR 通道在补偿错误估计方面贡献最大。
所提出的方法证明了 cbPPG 可应用于手术室内环境。无论考虑哪个身体部位，它都可以轻松转移到其他环境中。
