Pulse rate variability is a physiological parameter that has been extensively studied and correlated with many physical ailments. However, the phase relationship between inter-beat interval, IBI, and breathing has very rarely been studied. Develop a technique by which the phase relationship between IBI and breathing can be accurately and efficiently extracted from photoplethysmography (PPG) data. A program based on Lock-in Amplifier technology was written in Python to implement a novel technique, Dynamic Phase Extraction. It was tested using a breath pacer and a PPG sensor on 6 subjects who followed a breath pacer at varied breathing rates. The data were then analyzed using both traditional methods and the novel technique (Dynamic Phase Extraction) utilizing a breath pacer. Pulse data was extracted using a PPG sensor. Dynamic Phase Extraction (DPE) gave the magnitudes of the variation in IBI associated with breathing \((\Delta IBI)\) measured with photoplethysmography during paced breathing (with premature ventricular contractions, abnormal arrhythmias, and other artifacts edited out). \(\Delta IBI\) correlated well with two standard measures of pulse rate variability: the Standard Deviation of the inter-beat interval (SDNN) (ρ = 0.911) and with the integrated value of the Power Spectral Density between 0.04 and 0.15 Hz (Low Frequency Power or LF Power) (ρ = 0.885). These correlations were comparable to the correlation between the SDNN and the LF Power (ρ = 0.877). In addition to the magnitude \(\Delta IBI\), Dynamic Phase Extraction also gave the phase between the breath pacer and the changes in the inter-beat interval (IBI) due to respiratory sinus arrythmia (RSA), and correlated well with the phase extracted using a Fourier transform (ρ = 0.857). Dynamic Phase Extraction can extract both the phase between the breath pacer and the changes in IBI due to the respiratory sinus arrhythmia component of pulse rate variability (\(\Delta IBI)\), but is limited by needing a breath pacer.

脉搏率变异性是一种生理参数，已被广泛研究并与许多身体疾病相关。然而，几乎没有研究过节拍间间隔、IBI 和呼吸之间的相位关系。开发一种技术，通过该技术可以从光电容积描记 (PPG) 数据中准确有效地提取 IBI 和呼吸之间的相位关系。一个基于锁相放大器技术的程序是用 Python 编写的，以实现一种新技术，即动态相位提取。使用呼吸起搏器和 PPG 传感器对 6 名以不同呼吸频率跟随呼吸起搏器的受试者进行了测试。然后使用传统方法和使用呼吸起搏器的新技术（动态相位提取）分析数据。使用 PPG 传感器提取脉冲数据。\((\Delta IBI)\)在起搏呼吸期间用光电容积描记法测量（室性早搏、异常心律失常和其他伪影已被删除）。\(\Delta IBI\)与脉率变异性的两个标准测量值良好相关：搏动间期 (SDNN) 的标准偏差 (ρ = 0.911) 和 0.04 和 0.15 Hz 之间的功率谱密度的积分值（低频功率或 LF 功率）（ρ = 0.885）。这些相关性与 SDNN 和 LF 功率之间的相关性相当（ρ = 0.877）。除了幅度\(\Delta IBI\), 动态相位提取还给出了呼吸起搏器之间的相位和由于呼吸窦性心律失常 (RSA) 引起的节拍间间隔 (IBI) 的变化，并且与使用傅里叶变换 (ρ = 0.857) 提取的相位具有良好的相关性。动态相位提取可以提取呼吸起搏器之间的相位以及由于脉搏率变异性 ( \(\Delta IBI)\)的呼吸性窦性心律失常分量引起的 IBI 变化，但受限于需要呼吸起搏器。