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.

中文翻译：

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用于生命体征提取的光电体积描记信号的调制模型


本文介绍了一种幅度和频率调制 (AM-FM) 模型来表征光电体积描记法 (PPG) 信号。模型表明，PPG信号频谱包含一个主要频率分量——心率（HR），其两侧受到两个较弱频率分量的保护；从主导成分到保护成分的距离代表呼吸频率（RR）。基于该模型，提出了一种通过搜索主频率分量和两个保护分量来估计 HR 和 RR 的有效算法。所提出的方法在频域中执行来估计RR，这比基于时间特征的现有技术对加性噪声更加鲁棒。实验分别对接触式传感器（血氧计）和非接触式可见光成像传感器（彩色相机）收集的两种 PPG 信号进行了实验。来自非接触式传感器的 PPG 信号比来自接触式传感器的信号噪声大得多。实验结果证明了所提出算法的有效性，包括在相对嘈杂的场景下。