Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Abstract

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly.

Photoplethysmogram Analysis and Applications: An Integrative Review

“…In particular, attempts to find meaningful information from PPG using various deep learning models are continuously increasing. Representative applications of PPG analysis using deep learning include heart rate estimation ( Biswas et al, 2019 ; Reiss et al, 2019 ; Panwar et al, 2020 ; Chang et al, 2021 ; Mehrgardt et al, 2021 ), cuff-less blood pressure estimation ( Panwar et al, 2020 ; El-Hajj and Kyriacou, 2021a , b ; Schrumpf et al, 2021a , b ; Tazarv and Levorato, 2021 ), and arterial fibrillation prediction ( Poh et al, 2018 ; Kwon et al, 2019 ; Aschbacher et al, 2020 ; Cheng et al, 2020 ; Pereira et al, 2020 ). In addition, PPG-based deep learning models are being used for respiratory rate estimation ( Ravichandran et al, 2019 ), sleep monitoring ( Korkalainen et al, 2020 ), diabetes ( Avram et al, 2019 ), vascular aging estimation ( Dall’Olio et al, 2020 ), and peripheral arterial disease classification ( Allen et al, 2021 ).…”

Diagnostic Features and Potential Applications of PPG Signal in Healthcare: A Systematic Review

“…Wrist-worn bands [ 92 , 98 ], earlobe sensor [ 43 ] and even a special bathroom weighing scale [ 40 ] are used to estimate BP continuously. A couple of studies [ 97 , 100 , 101 , 102 ] monitor the BP iPPG from a camera; others used smartphone cameras [ 84 , 101 ].…”

Contactless Vital Signs Monitoring From Videos Recorded With Digital Cameras: An Overview

“…However, PTT allows only the estimation of the systolic blood pressure (SBP), and in one study, the PWV has been used to estimate SBP and DBP (diastolic blood pressure; Khong et al, 2017). However, it is worth mentioning that there are alternative methods to the above mentioned, Furthermore, in some studies Machine Learning and Deep Learning techniques are implemented to obtain an estimation of SBP and DBP (Chowdhury et al, 2020;el Hajj and Kyriacou, 2020;Rong and Li, 2021;Schrumpf et al, 2021).…”