Gao Haoyuan, Zhang Chao, Pei Shengbing, Wu Xiaopei
School of Computer Science and Technology, Anhui University, Hefei 230601, China.
Biomed Opt Express. 2023 Feb 13;14(3):1119-1136. doi: 10.1364/BOE.477143. eCollection 2023 Mar 1.
Remote photoplethysmogram (rPPG) is a low-cost method to extract blood volume pulse (BVP). Some crucial vital signs, such as heart rate (HR) and respiratory rate (RR) etc. can be achieved from BVP for clinical medicine and healthcare application. As compared to the conventional PPG methods, rPPG is more promising because of its non-contacted measurement. However, both BVP detection methods, especially rPPG, are susceptible to motion and illumination artifacts, which lead to inaccurate estimation of vital signs. Signal quality assessment (SQA) is a method to measure the quality of BVP signals and ensure the credibility of estimated physiological parameters. But the existing SQA methods are not suitable for real-time processing. In this paper, we proposed an end-to-end BVP signal quality evaluation method based on a long short-term memory network (LSTM-SQA). Two LSTM-SQA models were trained using the BVP signals obtained with PPG and rPPG techniques so that the quality of BVP signals derived from these two methods can be evaluated, respectively. As there is no publicly available rPPG dataset with quality annotations, we designed a training sample generation method with blind source separation, by which two kinds of training datasets respective to PPG and rPPG were built. Each dataset consists of 38400 high and low-quality BVP segments. The achieved models were verified on three public datasets (IIP-HCI dataset, UBFC-Phys dataset, and LGI-PPGI dataset). The experimental results show that the proposed LSTM-SQA models can effectively predict the quality of the BVP signal in real-time.
远程光电容积脉搏波图（rPPG）是一种低成本的提取血容量脉搏（BVP）的方法。一些关键生命体征，如心率（HR）和呼吸频率（RR）等，可以从BVP中获取，用于临床医学和医疗保健应用。与传统的PPG方法相比，rPPG因其非接触式测量而更具前景。然而，这两种BVP检测方法，尤其是rPPG，都容易受到运动和光照伪影的影响，这会导致生命体征估计不准确。信号质量评估（SQA）是一种测量BVP信号质量并确保估计生理参数可信度的方法。但现有的SQA方法不适用于实时处理。在本文中，我们提出了一种基于长短期记忆网络（LSTM-SQA）的端到端BVP信号质量评估方法。使用通过PPG和rPPG技术获得的BVP信号训练了两个LSTM-SQA模型，以便分别评估源自这两种方法的BVP信号的质量。由于没有带有质量注释的公开可用rPPG数据集，我们设计了一种基于盲源分离的训练样本生成方法，通过该方法构建了分别对应于PPG和rPPG的两种训练数据集。每个数据集由38400个高质量和低质量的BVP片段组成。在三个公共数据集（IIP-HCI数据集、UBFC-Phys数据集和LGI-PPGI数据集）上对所实现的模型进行了验证。实验结果表明，所提出的LSTM-SQA模型能够有效地实时预测BVP信号的质量。
