The Machine Learnings Leading the Cuffless PPG Blood Pressure Sensors into the Next Stage

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.