Remote blood pressure estimation using BVP signal features from facial videos

Abstract

Pulse signals contain abundant cardiovascular functional information and can be used for blood pressure estimation. Remote photoplethysmography (rPPG) technology offers a solution to obtain pulse signals from facial videos and then to achieve continuous blood pressure estimation. However, rPPG is susceptible to external factors that lead to a decrease in pulse signal quality, which directly affects the accuracy and reliability of blood pressure estimation. Therefore, this paper proposes a method that integrates advanced signal processing techniques and pulse feature analysis to improve the accuracy of video-based blood pressure estimation. First, we used an adaptive chirp mode decomposition algorithm and a waveform quality analysis algorithm based on a correlation coefficient to suppress noise interference and ensure the effectiveness of the pulse features obtained. Then, we conducted pulse signal feature selection using the mean impact value algorithm and established a blood pressure estimation model based on a BP neural network. Finally, we updated the neural network BP using the sparrow search algorithm to obtain the optimal blood pressure estimation model. Through validation on a private dataset, the results show that the proposed method can meet the blood pressure measurement standards and effectively achieve remote blood pressure estimation.