Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and increasingly used for in a variety of research and clinical application to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers. This work describes the creation of a standard Python toolbox, denoted pyPPG, for long-term continuous PPG time series analysis recorded using a standard finger-based transmission pulse oximeter. The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2,054 adult polysomnography recordings totaling over 91 million reference beats. This algorithm outperformed the open-source original Matlab implementation by ~5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3,000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points. Based on these fiducial points, pyPPG engineers a set of 74 PPG biomarkers. Studying the PPG time series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on physiozoo.org

翻译：光电容积描记术是一种无创光学技术，用于测量组织内血容量的变化。该技术正被日益广泛地应用于各类研究与临床场景，以评估血管动力学和生理参数。然而，与心率变异性测量不同——该领域已建立了稳定的标准并开发了先进的分析工具箱与软件——目前尚缺乏针对连续光电容积脉搏波（PPG）分析的标准方法与开源工具。因此，本研究的主要目标是识别、标准化、实现并验证关键数字化PPG生物标志物。本文描述了标准Python工具箱pyPPG的创建过程，该工具专用于使用基于手指的标准透射式脉搏血氧仪记录的长期连续PPG时间序列分析。在对2,054例成人多导睡眠图记录（涵盖超过9100万个参考心跳）进行基准测试时，改进后的PPG峰值检测器F1分数达88.19%，优于当前最新方法。在随机选取的100份MESA记录子集上，该算法性能较开源原始Matlab实现提升约5%。为验证标志点检测器的性能，两名标注员手动标注了超过3,000个标志点。该检测器在所有标志点上均表现出色，平均绝对误差低于10毫秒。基于这些标志点，pyPPG工程化构建了包含74项PPG生物标志物的特征集。通过pyPPG研究PPG时间序列的变异性，可加深对疾病表现与病因学的理解。该工具箱还可用于训练数据驱动模型时的生物标志物工程。pyPPG已在physiozoo.org平台开放获取。