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.
Highlights
M ONITORING vital signs, such as heart rate (HR) and respiratory rate (RR), is essential in understanding a patient’s physiological condition and monitoring and diagnosing diseases related to cardiovascular and lung functions
We conducted experiments to demonstrate the effectiveness of the proposed AM-FM method on two PPG signal datasets – a contact PPG (cPPG) dataset collected with a contact oximeter and an remote PPG (rPPG) dataset captured with a color camera
It is worth noting that the rPPG signal is much noisier than the cPPG signal, due to the distance between human skin and the sensor and the subject’s voluntary movements
Summary
M ONITORING vital signs, such as heart rate (HR) and respiratory rate (RR), is essential in understanding a patient’s physiological condition and monitoring and diagnosing diseases related to cardiovascular and lung functions. Considering the combined effect of RIIV, RIAV, and RIFV, time-domain approaches have been proposed to improve the accuracy of RR estimation [4], [10] In these studies, the three respiratory-induced variations are first extracted from the PPG signal in the time domain and used individually to produce three seperate RR estimates. To improve the PPG signal analysis technology, we have developed a frequency-domain method based on a modulation model to extract RIAV and RIFV features from the PPG spectrum to estimate HR and RR. The motivation for this derives from the respiratory-induced effects on PPG signals and the observation of three noticeable signal traces in PPG spectrograms (Fig. 2).
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