# A Deep Learning Approach to Predict Blood Pressure from PPG Signals

arXiv: https://arxiv.org/abs/2108.00099 · HTML: https://mlsanity.com/paper/2108.00099/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 30 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)

## Built on by (most cited first, 6 of 6)

- [Cuff-less Arterial Blood Pressure Waveform Synthesis from Single-site PPG using Transformer & Frequency-domain Learning](https://mlsanity.com/paper/2401.05452/) (2024)
- [Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks](https://mlsanity.com/paper/2201.09976/) (2022)
- ["Can't Take the Pressure?": Examining the Challenges of Blood Pressure Estimation via Pulse Wave Analysis](https://mlsanity.com/paper/2304.14916/) (2023)
- [Estimating Clinical Lab Test Result Trajectories from PPG using Physiological Foundation Model and Patient-Aware State Space Model -- a UNIPHY+ Approach](https://mlsanity.com/paper/2509.16345/) (2025)
- [Cuffless Blood Pressure Estimation from Six Wearable Sensor Modalities in Multi-Motion-State Scenarios](https://mlsanity.com/paper/2512.01653/) (2025)
- [Deep Learning Classification of Photoplethysmogram Signal for Hypertension Levels](https://mlsanity.com/paper/2405.14556/) (2024)

## Abstract (arXiv)

Blood Pressure (BP) is one of the four primary vital signs indicating the status of the body's vital (life-sustaining) functions. BP is difficult to continuously monitor using a sphygmomanometer (i.e. a blood pressure cuff), especially in everyday-setting. However, other health signals which can be easily and continuously acquired, such as photoplethysmography (PPG), show some similarities with the Aortic Pressure waveform. Based on these similarities, in recent years several methods were proposed to predict BP from the PPG signal. Building on these results, we propose an advanced personalized data-driven approach that uses a three-layer deep neural network to estimate BP based on PPG signals. Different from previous work, the proposed model analyzes the PPG signal in time-domain and automatically extracts the most critical features for this specific application, then uses a variation of recurrent neural networks called Long-Short-Term-Memory (LSTM) to map the extracted features to the BP value associated with that time window. Experimental results on two separate standard hospital datasets, yielded absolute errors mean and absolute error standard deviation for systolic and diastolic BP values outperforming prior works.