Chen Shutao, Wong Kwan-Long, Chin Jing-Wei, Chan Tsz-Tai, So Richard H Y
PanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China.
Department of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
Bioengineering (Basel). 2024 Jul 23;11(8):743. doi: 10.3390/bioengineering11080743.
Remote photoplethysmography (rPPG) is an emerging non-contact method for monitoring cardiovascular health based on facial videos. The quality of the captured videos largely determines the efficacy of rPPG in this application. Traditional rPPG techniques, while effective for heart rate (HR) estimation, often produce signals with an inadequate signal-to-noise ratio (SNR) for reliable vital sign measurement due to artifacts like head motion and measurement noise. Another pivotal factor is the overlooking of the inherent properties of signals generated by rPPG (rPPG-signals). To address these limitations, we introduce DiffPhys, a novel deep generative model particularly designed to enhance the SNR of rPPG-signals. DiffPhys leverages the conditional diffusion model to learn the distribution of rPPG-signals and uses a refined reverse process to generate rPPG-signals with a higher SNR. Experimental results demonstrate that DiffPhys elevates the SNR of rPPG-signals across within-database and cross-database scenarios, facilitating the extraction of cardiovascular metrics such as HR and HRV with greater precision. This enhancement allows for more accurate monitoring of health conditions in non-clinical settings.
远程光电容积脉搏波描记法（rPPG）是一种基于面部视频监测心血管健康的新兴非接触式方法。所捕获视频的质量在很大程度上决定了rPPG在此应用中的效果。传统的rPPG技术虽然对心率（HR）估计有效，但由于头部运动和测量噪声等伪影，通常会产生信噪比（SNR）不足的信号，难以进行可靠的生命体征测量。另一个关键因素是忽视了rPPG产生的信号（rPPG信号）的固有特性。为了解决这些局限性，我们引入了DiffPhys，这是一种新颖的深度生成模型，专门设计用于提高rPPG信号的信噪比。DiffPhys利用条件扩散模型学习rPPG信号的分布，并使用改进的反向过程生成具有更高信噪比的rPPG信号。实验结果表明，DiffPhys在数据库内和跨数据库场景中均提高了rPPG信号的信噪比，有助于更精确地提取诸如HR和HRV等心血管指标。这种增强使得在非临床环境中能够更准确地监测健康状况。
