Autocorrelation-based, passive, non-contact, photoplethysmography: Computationally-efficient, noise-tolerant, extraction of heart rates from video

Abstract

Photoplethysmography (PPG) is the detection of blood flow or pressure by optical means. The most common method involves direct skin-contact measurement of light from an LED. However, the small color changes in skin under normal lighting conditions, as recorded by conventional video, potentially allow passive, noncontact, PPG. Eulerian Video Magnification (EVM) was used to demonstrate that small color changes in a subject’s face can be amplified to make them visible to a human observer. A variety of methods have been applied to extract heart rate from video. The signal obtained by PPG is not a simple sinusoid, but has a relatively complex structure, which in video is degraded by ambient lighting variations, motion, noise, and a low sampling rate. Although EVM and many other analysis methods in the literature essentially operate in the frequency domain, fitting the video data to their model requires extensive preprocessing. In this paper a time-based autocorrelation method is applied directly to the video signal that exhibits superior noise rejection and resolution for detecting quasi-periodic waveforms. The method described in the current work avoids both the preprocessing computational cost and the potential signal distortions.