# Assessment of deep learning based blood pressure prediction from PPG and rPPG signals

arXiv: https://arxiv.org/abs/2104.09313 · HTML: https://mlsanity.com/paper/2104.09313/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 60 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Continuous PPG-Based Blood Pressure Monitoring Using Multi-Linear Regression](https://mlsanity.com/paper/2011.02231/) (2020)
- [Cuffless Blood Pressure Estimation from Electrocardiogram and Photoplethysmogram Using Waveform Based ANN-LSTM Network](https://mlsanity.com/paper/1811.02214/) (2018)
- [Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks](https://mlsanity.com/paper/2007.12802/) (2020)

## Built on by (most cited first, 9 of 9)

- [PaPaGei: Open Foundation Models for Optical Physiological Signals](https://mlsanity.com/paper/2410.20542/) (2024)
- [rPPG-Toolbox: Deep Remote PPG Toolbox](https://mlsanity.com/paper/2210.00716/) (2022)
- [HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Series](https://mlsanity.com/paper/2510.25785/) (2025)
- [RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation](https://mlsanity.com/paper/2208.08672/) (2022)
- [RGB Camera-based Physiological Sensing: Challenges and Future Directions](https://mlsanity.com/paper/2110.13362/) (2021)
- [Semi-rPPG: Semi-Supervised Remote Physiological Measurement with Curriculum Pseudo-Labeling](https://mlsanity.com/paper/2502.03855/) (2025)
- [Hypertension Detection From High-Dimensional Representation of Photoplethysmogram Signals](https://mlsanity.com/paper/2308.02425/) (2023)
- [Factors that affect Camera based Self-Monitoring of Vitals in the Wild](https://mlsanity.com/paper/2301.12943/) (2023)
- [U-FaceBP: Uncertainty-aware Bayesian Ensemble Deep Learning for Face Video-based Blood Pressure Estimation](https://mlsanity.com/paper/2412.10679/) (2024)

## Abstract (arXiv)

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 apply this parameterization to a larger PPG dataset and train NNs to predict BP. The resulting prediction errors increase towards less frequent BP values. 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 a personalization technique and retrain our NNs with subject-specific data. This slightly reduces the prediction errors.