Peripheral venous blood oxygen saturation can be non-invasively estimated using photoplethysmography

A Systematic Approach Focused on Machine Learning Models for Exploring the Landscape of Physiological Measurement and Estimation Using Photoplethysmography (PPG)

A non-invasive optical technique known as photoplethysmography (PPG) can be used to provide various physiological measurements and estimations. PPG can be used to assess cardiovascular disease (CVD). Hypertension is a primary risk factor for CVD and a major health problem worldwide. PPG is popular because of its important applications in the evaluation of cardiac activity, variations in venous blood volume, blood oxygen saturation, blood pressure and heart rate variability, etc. In this study, we provide a comprehensive analysis of the extraction of various physiological parameters using PPG waveforms. In addition, we focused on the role of machine learning (ML) models used for the estimation of blood pressure and hypertension classification based on PPG waveforms to make future research and innovation recommendations. This study will be helpful for researchers, scientists, and medical practitioners working on PPG waveforms for monitoring, screening, and diagnosis, as a comparative study or reference.

Cuff-Less Blood Pressure Prediction Based on Photoplethysmography and Modified ResNet

Cardiovascular disease (CVD) has become a common health problem of mankind, and the prevalence and mortality of CVD are rising on a year-to-year basis. Blood pressure (BP) is an important physiological parameter of the human body and also an important physiological indicator for the prevention and treatment of CVD. Existing intermittent measurement methods do not fully indicate the real BP status of the human body and cannot get rid of the restraining feeling of a cuff. Accordingly, this study proposed a deep learning network based on the ResNet34 framework for continuous prediction of BP using only the promising PPG signal. The high-quality PPG signals were first passed through a multi-scale feature extraction module after a series of pre-processing to expand the perceptive field and enhance the perception ability on features. Subsequently, useful feature information was then extracted by stacking multiple residual modules with channel attention to increase the accuracy of the model. Lastly, in the training stage, the Huber loss function was adopted to stabilize the iterative process and obtain the optimal solution of the model. On a subset of the MIMIC dataset, the errors of both SBP and DBP predicted by the model met the AAMI standards, while the accuracy of DBP reached Grade A of the BHS standard, and the accuracy of SBP almost reached Grade A of the BHS standard. The proposed method verifies the potential and feasibility of PPG signals combined with deep neural networks in the field of continuous BP monitoring. Furthermore, the method is easy to deploy in portable devices, and it is more consistent with the future trend of wearable blood-pressure-monitoring devices (e.g., smartphones and smartwatches).

A Review of Photoplethysmography-based Physiological Measurement and Estimation, Part 2: Multi-input Methods

Photoplethysmography can be used to estimate many physiological parameters based on features extracted from the measured waveform. Following the single parameter estimations that have been reviewed in part 1 of this paper, we here review methods where the waveform is used in conjunction with other measured physiological signals. Being a low-cost, non-invasive, and user friendly technique, many PPG-based physiological data extraction methods are being researched. The parameters reviewed that can be estimated using the PPG waveform plus additional inputs include cardiac output, blood pressure, venous function assessment, blood oxygen saturation, and fetal heart rate and fetal oxygen saturation. The different processing techniques, algorithms and methods are reviewed in addition to providing a comparison of results with the reference standards to validate the different methods. Future research considerations for each parameter estimation are also discussed. This paper could be helpful for future research on PPG based wearable devices for physiological multi-parameter estimations.