Wei Bing, Wu Xiaopei, Zhang Chao, Lv Zhao
Anhui Province Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, 230601, China.
Department of Computer Science and Technology, Hefei Normal College, Hefei 230601, China.
Biomed Opt Express. 2021 Jul 23;12(8):5227-5245. doi: 10.1364/BOE.423508. eCollection 2021 Aug 1.
Peripheral oxygen saturation (SpO2), a vital physiological sign employed in clinical care, is commonly obtained by using a contact pulse oximeter. With the rapid popularization of ordinary red-green-blue (RGB) webcams embedded in devices such as smartphones or laptops, there are broad application prospects for exploring techniques for non-contact SpO2 extraction using RGB webcams. However, many issues remain to be solved in the traditional webcam-based SpO2 extraction methods, such as the inherent low signal-to-noise ratio (SNR) of alternating current (AC) components of RGB signals and the potential defects in using RGB signals combination for SpO2 extraction. In this study, we conducted an in-depth examination of the existing research on webcam-based SpO2 extraction techniques, analyzed the practical problems in using them, and explored new ideas to solve the problems. Rather than roughly using the standard deviations (SD) of AC components for calculations, we performed blind source separation for AC components, and then used the energy coefficients retained in the mixed matrix to replace the variables required in the algorithm. Moreover, steady data was selected to compensate for the potential defects in using RGB signals combination. Through these efforts, the anti-noise capability of the algorithm was significantly enhanced, and the related defects were compensated for. The experimental results indicated that the proposed method produced reliable SpO2 estimation that could potentially-with further research-be used in real applications.
外周血氧饱和度（SpO2）是临床护理中使用的一项重要生理指标，通常通过使用接触式脉搏血氧仪来获取。随着智能手机或笔记本电脑等设备中嵌入的普通红绿蓝（RGB）网络摄像头的迅速普及，探索利用RGB网络摄像头进行非接触式SpO2提取技术具有广阔的应用前景。然而，传统的基于网络摄像头的SpO2提取方法仍有许多问题有待解决，例如RGB信号交流（AC）分量固有的低信噪比（SNR）以及使用RGB信号组合进行SpO2提取时的潜在缺陷。在本研究中，我们对现有的基于网络摄像头的SpO2提取技术研究进行了深入考察，分析了使用这些技术时的实际问题，并探索了解决这些问题的新思路。我们不是粗略地使用AC分量的标准差（SD）进行计算，而是对AC分量进行盲源分离，然后使用混合矩阵中保留的能量系数来替代算法所需的变量。此外，选择稳定的数据来弥补使用RGB信号组合时的潜在缺陷。通过这些努力，算法的抗噪能力得到显著增强，相关缺陷也得到了弥补。实验结果表明，所提出的方法能够产生可靠的SpO2估计值，经过进一步研究，有望应用于实际场景。
