Recent works on the machine learnings designed for cuffless blood pressure (BP) estimation based on measured photoplethysmogram (PPG) waveforms are reviewed by this study, with future trends of the related technology developments distilled. This review starts with those based on the conventional pulse wave velocity (PWV) theory, by which few equations are derived to calculate BPs based on measured pulse arrival times (PATs) and/or pulse transit time (PTT). Due to the inadequacy of PATs and PPTs to characterize BP, some works were reported to employ more features in PPG waveforms to achieve better accuracy. In these works, varied machine learnings were adopted, such as support vector machine (SVM), regression tree (RT), adaptive boosting (AdaBoost), and artificial neural network (ANN), etc., resulting in satisfactory accuracies based on a large number of data in the databases of Queensland and/or MIMIC II. Most recently, a few studies reported to utilize the deep learning machines like convolution neural network (CNN), recursive neural network (RNN), and long short-term memory (LSTM), etc., to handle feature extraction and establish models integrally, with the aim to cope with the inadequacy of pre-determined (hand-crafted) features to characterize BP and the difficulty of extracting pre-determined features by a designed algorithm. Therefore, the deep learning opens an opportunity of achieving much better BP accuracy by using a single PPG sensor. Favorable accuracies have been resulted by these few studies in comparison with prior works. Finally, future research efforts needed towards successful commercialization of the cuffless BP sensor are distilled.

中文翻译：

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机器学习引领无袖 PPG 血压传感器进入下一阶段

本研究回顾了基于测量的光体积描记 (PPG) 波形的无袖带血压 (BP) 估计机器学习的最新工作，并总结了相关技术发展的未来趋势。这篇综述从基于传统脉搏波速度 (PWV) 理论的那些理论开始，通过这些理论推导出很少的方程来计算基于测量的脉冲到达时间 (PAT) 和/或脉冲传播时间 (PTT) 的 BP。由于 PAT 和 PPT 不足以表征 BP，据报道，一些工作在 PPG 波形中采用更多特征以实现更好的准确性。在这些工作中，采用了各种机器学习，如支持向量机（SVM）、回归树（RT）、自适应提升（AdaBoost）和人工神经网络（ANN）等，根据昆士兰和/或 MIMIC II 数据库中的大量数据得出令人满意的准确度。最近，一些研究报告利用深度学习机器，如卷积神经网络 (CNN)、递归神经网络 (RNN) 和长短期记忆 (LSTM) 等，来整体处理特征提取和建立模型，旨在解决预先确定的（手工制作的）特征不足以表征BP以及通过设计的算法提取预先确定的特征的困难。因此，深度学习为通过使用单个 PPG 传感器实现更好的 BP 精度提供了机会。与先前的工作相比，这些少数研究得出了良好的准确性。最后，