Modulation Model of the Photoplethysmography Signal for Vital Sign Extraction

Abstract

This paper introduces an amplitude and frequency modulation (AM-FM) model to characterize the photoplethysmography (PPG) signal. The model indicates that the PPG signal spectrum contains one dominant frequency component-the heart rate (HR), which is guarded by two weaker frequency components on both sides; the distance from the dominant component to the guard components represents the respiratory rate (RR). Based on this model, an efficient algorithm is proposed to estimate both HR and RR by searching for the dominant frequency component and two guard components. The proposed method is performed in the frequency domain to estimate RR, which is more robust to additive noise than the prior art based on temporal features. Experiments were conducted on two types of PPG signals collected with a contact sensor (an oximeter) and a contactless visible imaging sensor (a color camera), respectively. The PPG signal from the contactless sensor is much noisier than the signal from the contact sensor. The experimental results demonstrate the effectiveness of the proposed algorithm, including under relatively noisy scenarios.

Dynamical Analysis of Biological Signals with the 0–1 Test: A Case Study of the PhotoPlethysmoGraphic (PPG) Signal

“…As we will show later, the mutual coupling between the different components is intricate and operates at different timescales to regulate blood volume based on physiological needs [41][42][43][44]. At small timescales, the regular cardiac rhythm quasi-periodically modulated by the breathing rhythm prevails [37,45,46], as reflected in the two prominent peaks in the PSD of a typical PPG signal, relating to the respiratory, lower frequency peak, and heart, higher frequency peak, rates (see Figure 2f-j).…”

“…This paper focuses only on a single biological signal, the PhotoPlethysmoGraphic (PPG) signal; forward publications will describe the results for more biological signals. We have chosen the PPG signal because it is easily accessible, and the information provided allows us to monitor vital physiological signs [37].…”

PulseEdit: Editing Physiological Signals in Facial Videos for Privacy Protection

“…For instance, remote photoplethysmography (rPPG) technology has attracted a growing amount of interests in capturing the subtle color changes of the skin caused by heartbeats in facial videos under ambient light. We can further infer heart rate (HR) [5]- [10], respiration rate (RR) [11], [12], and heart rate variability Mingliang Chen was with the Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742 USA when he performed the work, and now he is with Meta Platform Inc., Menlo Park, CA 94025 USA (e-mail: mchen126@terpmail.umd.edu).…”

Remote Blood Oxygen Estimation From Videos Using Neural Networks

“…These subtle changes can be captured without contact using ubiquitous cameras, such as low-cost webcams and smartphone cameras. Such noncontact video-based measurement contains important health-related information of a person and provides an agile way to monitor health vital signs, including heart rate [9,17,32,37], breathing rate [6,23], and heart rate variability [14,23,11], as an emerging class of biometrics enabled by computer vision techniques.…”