ArticleSensors (Basel, Switzerland)2021
Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Wearable Blood Pressure Monitoring Devices: Understanding Heterogeneity in Design and Evaluation.IEEE transactions on bio-medical engineering · 2024Pooled it
- Piezoionic copolymer ionogel with tunable hydrogen bond enables epidermal blood pressure analyzer.Science advances · 2026Article
- The role of dataset integrity, calibration and signal quality in neural network-based blood pressure estimation.Scientific reports · 2026Article
- From Cuffs to Code: Machine Learning in Non-Invasive Blood Pressure Monitoring.Anaesthesia, critical care & pain medicine · 2026Review
- Clinical Validation of rPPG-Enabled Contactless Pulse Rate Monitoring Software in Cardiovascular Disease Patients.Bioengineering (Basel, Switzerland) · 2026Article
- Optical methods for cuffless blood pressure measurements.Biophotonics discovery · 2026Review
- Speckle contrast optical spectroscopy for cuffless blood pressure estimation based on microvascular blood flow and volume oscillations.Biomedical optics express · 2025Article
- Optimizing Input Feature Sets Using Catch-22 and Personalization for an Accurate and Reliable Estimation of Continuous, Cuffless Blood Pressure.Bioengineering (Basel, Switzerland) · 2025Article
- Remote photoplethysmography for health assessment: a review informed by IntelliProve technology.Frontiers in digital health · 2025Review
- Photoplethysmography-based non-invasive blood pressure monitoring via ensemble model and imbalanced dataset processing.Physical and engineering sciences in medicine · 2024Article
- Machine Learning-Based Cardiac Output Estimation Using Photoplethysmography in Off-Pump Coronary Artery Bypass Surgery.Journal of clinical medicine · 2024Article
- Article
- Accurate and 30-plus days reliable cuffless blood pressure measurements with 9-minutes personal photoplethysmograph data and mixed deduction learning.Scientific reports · 2024Article
- Continuous blood pressure prediction system using Conv-LSTM network on hybrid latent features of photoplethysmogram (PPG) and electrocardiogram (ECG) signals.Scientific reports · 2024Article
- SleepBP-Net: A Time-Distributed Convolutional Network for Nocturnal Blood Pressure Estimation from Photoplethysmogram.IEEE sensors journal · 2024Article
- Generalized channel separation algorithms for accurate camera-based multi-wavelength PTT and BP estimation.Biomedical optics express · 2024Article
- Improving accuracy of vascular access quality classification in hemodialysis patients using deep learning with K highest score feature selection.The Journal of international medical research · 2024Article
- Fair non-contact blood pressure estimation using imaging photoplethysmography.Biomedical optics express · 2024Article
- Fundamental and Practical Feasibility of Electrocardiogram Reconstruction from Photoplethysmogram.Sensors (Basel, Switzerland) · 2024Article
- Article
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Authors and funding
5 authors.
Funding
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.
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