Contactless Hemodynamic Monitoring based on Multi-scale Gaussian Filtering via Imaging PPG

Imaging photoplethysmography (iPPG) is an emerging optical technique that allows for the contactless acquisition of arterial Blood Volume Pulse (BVP) signals from video recordings of the human skin. While iPPG offers a non-contact and convenient means for physiological monitoring, the accuracy of the extracted BVP signals remains limited. This limitation hinders its potential for advanced cardiovascular assessments, such as evaluations of arterial stiffness and cardiac function. To address this issue, we propose a novel physiologically informed Gaussian filtering method, based on the prior knowledge that the BVP waveform can be modeled as a mixture of multiple Gaussian components. Specifically, a set of physiological Gaussian kernels is employed to convolve the noisy iPPG signal, generating a Gaussian representation that emphasizes waveform components with physiological relevance. This representation is further refined by a Transformer-based neural network to reconstruct accurate BVP signals. Experimental results demonstrate a notable improvement in BVP accuracy, with the mean absolute error reducing from 0.25 to 0.08. This enhancement in iPPG precision highlights the potential of our approach for advanced medical applications.