Non-invasive arterial blood pressure measurement and SpO2 estimation using PPG signal: a deep learning framework

Abstract

Background Monitoring blood pressure and peripheral capillary oxygen saturation plays a crucial role in healthcare management for patients with chronic diseases, especially hypertension and vascular disease. However, current blood pressure measurement methods have intrinsic limitations; for instance, arterial blood pressure is measured by inserting a catheter in the artery causing discomfort and infection. Method Photoplethysmogram (PPG) signals can be collected via non-invasive devices, and therefore have stimulated researchers’ interest in exploring blood pressure estimation using machine learning and PPG signals as a non-invasive alternative. In this paper, we propose a Transformer-based deep learning architecture that utilizes PPG signals to conduct a personalized estimation of arterial systolic blood pressure, arterial diastolic blood pressure, and oxygen saturation. Results The proposed method was evaluated with a subset of 1,732 subjects from the publicly available ICU dataset MIMIC III. The mean absolute error is 2.52 ± 2.43 mmHg for systolic blood pressure, 1.37 ± 1.89 mmHg for diastolic blood pressure, and 0.58 ± 0.79% for oxygen saturation, which satisfies the requirements of the Association of Advancement of Medical Instrumentation standard and achieve grades A for the British Hypertension Society standard. Conclusions The results indicate that our model meets clinical standards and could potentially boost the accuracy of blood pressure and oxygen saturation measurement to deliver high-quality healthcare.

Preserving shape details of pulse signals for video-based blood pressure estimation

“…Six BP estimation models using pulse signals are selected. These models are SVR [ 16 ], FCN [ 18 ], LSTM [ 38 ], AlexNet [ 39 ], ResNet50 [ 39 ], and Transformer [ 40 ] respectively. SVR, FCN, and LSTM take pulse features as input, while AlexNet, ResNet50, and Transformer take a segment of the pulse signal as input.…”

“…Test results show that, compared to other iPPG pulse extraction methods, our method effectively reduces the error of both SBP and DBP estimation. We also take the results of the Transformer [ 40 ] model as an example of a more comprehensive assessment using the British Hypertension Society (BHS) grading criteria. According to Table 4 , our method shows obvious improvements in all error magnitudes.…”

“…However, the above BP estimation methods generally performed poorly in the dataset of this paper, and even the best-performing Transformer [ 40 ] model failed to meet the AAMI/ESH standard (MAE 5mmHg, STD 8mmHg). For the BHS standard, only the accuracy of DBP can barely close to grade C, failing to meet the medical device standard.…”

“…The error of SBP and DBP are also calculated to assess the influence of these factors on BP estimation. Here we choose Transformer [ 40 ] as the estimation model (shown in Table 6 ). In the test, baseline is our proposed method.…”

Comparative Analysis of Perfusion Index and End-Tidal Carbon Dioxide in Cardiac Arrest Patients: Implications for Hemodynamic Monitoring and Resuscitation Outcomes

“…Photoplethysmography (PPG) is another monitoring technique and has long been widely used in many fields of medicine, including hemodynamic monitoring in the form of pulse oximetry [8,9]. The perfusion index (PI) is a calculation used for hemodynamic monitoring.…”