Wei Bing, He Xuan, Zhang Chao, Wu Xiaopei
School of Computer Science and Technology, Anhui University, Hefei, 230601, China.
Department of Computer Science and Technology, Hefei Normal College, Hefei, 230061, China.
Biomed Eng Online. 2017 Jan 17;16(1):17. doi: 10.1186/s12938-016-0300-0.
Currently, many imaging photoplethysmography (IPPG) researches have reported non-contact measurements of physiological parameters, such as heart rate (HR), respiratory rate (RR), etc. However, it is accepted that only HR measurement has been mature for applications, and other estimations are relatively incapable for reliable applications. Thus, it is worth keeping on persistent studies. Besides, there are some issues commonly involved in these approaches need to be explored further. For example, motion artifact attenuation, an intractable problem, which is being attempted to be resolved by sophisticated video tracking and detection algorithms.
This paper proposed a blind source separation-based method that could synchronously measure RR and HR in non-contact way. A dual region of interest on facial video image was selected to yield 6-channels Red/Green/Blue signals. By applying Second-Order Blind Identification algorithm to those signals generated above, we obtained 6-channels outputs that contain blood volume pulse (BVP) and respiratory motion artifact. We defined this motion artifact as respiratory signal (RS). For the automatic selections of the RS and BVP among these outputs, we devised a kurtosis-based identification strategy, which guarantees the dynamic RR and HR monitoring available.
The experimental results indicated that, the estimation by the proposed method has an impressive performance compared with the measurement of the commercial medical sensors.
The proposed method achieved dynamic measurement of RR and HR, and the extension and revision of it may have the potentials for more physiological signs detection, such as heart rate variability, eye blinking, nose wrinkling, yawn, as well as other muscular movements. Thus, it might provide a promising approach for IPPG-based applications such as emotion computation and fatigue detection, etc.
目前，许多成像光电容积脉搏波描记法（IPPG）研究报告了对心率（HR）、呼吸频率（RR）等生理参数的非接触式测量。然而，人们公认只有心率测量在应用中已经成熟，其他估计相对无法可靠应用。因此，值得持续进行研究。此外，这些方法中普遍存在的一些问题需要进一步探索。例如，运动伪影衰减是一个棘手的问题，正在尝试通过复杂的视频跟踪和检测算法来解决。
本文提出了一种基于盲源分离的方法，该方法可以以非接触方式同步测量RR和HR。在面部视频图像上选择一个双感兴趣区域，以产生6通道红/绿/蓝信号。通过将二阶盲辨识算法应用于上述生成的信号，我们获得了包含血容量脉搏（BVP）和呼吸运动伪影的6通道输出。我们将这种运动伪影定义为呼吸信号（RS）。为了在这些输出中自动选择RS和BVP，我们设计了一种基于峰度的识别策略，以确保能够进行动态RR和HR监测。
实验结果表明，与商用医疗传感器的测量相比，所提出方法的估计具有令人印象深刻的性能。
所提出的方法实现了RR和HR的动态测量，对其进行扩展和改进可能具有检测更多生理体征的潜力，如心率变异性、眨眼、皱鼻、打哈欠以及其他肌肉运动。因此，它可能为基于IPPG的应用，如情感计算和疲劳检测等，提供一种有前途的方法。
