Estimating blood pressure using video-based PPG and deep learning

Abstract

This paper introduces a novel pipeline for estimating systolic and diastolic blood pressure using remote photoplethysmographic (rPPG) signals derived from video recordings of subjects’ faces. The pipeline consists of three main stages: rPPG signal extraction, denoising to transform the rPPG signal into a PPG-like waveform, and blood pressure estimation. This approach directly addresses the current lack of datasets that simultaneously include video, rPPG, and blood pressure data. To overcome this, the proposed pipeline leverages the extensive availability of PPG-based blood pressure estimation techniques, in combination with state-of-the-art algorithms for rPPG extraction, enabling the generation of reliable PPG-like signals from video input.

To validate the pipeline, we conducted comparative analyses with state-of-the-art methods at each stage and collected a dedicated dataset through controlled laboratory experimentation. The results demonstrate that the proposed solution effectively captures blood pressure information, achieving a mean error of 9.2 ± 11.3 mmHg for systolic and 8.6 ± 9.1 mmHg for diastolic blood pressure. Moreover, the denoised rPPG signals show a strong correlation with conventional PPG signals, supporting the reliability of the transformation process. This non-invasive and contactless method offers considerable potential for long-term blood pressure monitoring, particularly in Ambient Assisted Living (AAL) systems, where unobtrusive and continuous health monitoring is essential.