# Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

arXiv: https://arxiv.org/abs/2102.12245 · HTML: https://mlsanity.com/paper/2102.12245/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 24 Feb 2021.

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

- [FedGAN: Federated Generative Adversarial Networks for Distributed Data](https://mlsanity.com/paper/2006.07228/) (2020)
- [CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG](https://mlsanity.com/paper/2010.00104/) (2020)
- [PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks](https://mlsanity.com/paper/2005.01669/) (2020)
- [Optimised Convolutional Neural Networks for Heart Rate Estimation and Human Activity Recognition in Wrist Worn Sensing Applications](https://mlsanity.com/paper/2004.00505/) (2020)

## Built on by (most cited first, 12 of 12)

- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges](https://mlsanity.com/paper/2211.07893/) (2022)
- [FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning](https://mlsanity.com/paper/2310.16538/) (2023)
- [Federated Remote Physiological Measurement with Imperfect Data](https://mlsanity.com/paper/2203.05759/) (2022)
- [Generation of Synthetic Electronic Health Records Using a Federated GAN](https://mlsanity.com/paper/2109.02543/) (2021)
- [Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning](https://mlsanity.com/paper/2302.08688/) (2023)
- [Cuff-less Arterial Blood Pressure Waveform Synthesis from Single-site PPG using Transformer & Frequency-domain Learning](https://mlsanity.com/paper/2401.05452/) (2024)
- [Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading](https://mlsanity.com/paper/2302.06089/) (2023)
- [FedWOA: A Federated Learning Model that uses the Whale Optimization Algorithm for Renewable Energy Prediction](https://mlsanity.com/paper/2309.10337/) (2023)
- [Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Review](https://mlsanity.com/paper/2405.13832/) (2024)
- [Federated Time Series Generation on Feature and Temporally Misaligned Data](https://mlsanity.com/paper/2410.21072/) (2024)
- [Constraint Latent Space Matters: An Anti-anomalous Waveform Transformation Solution from Photoplethysmography to Arterial Blood Pressure](https://mlsanity.com/paper/2402.17780/) (2024)

## Abstract (arXiv)

Ischemic heart disease is the highest cause of mortality globally each year. This not only puts a massive strain on the lives of those affected but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart doctors commonly use electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, in particular when continuous arterial blood pressure (ABP) readings are taken and not to mention very costly. Using machine learning methods we seek to develop a framework that is capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time series-to-time series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.54 mmHg and a standard deviation of 23.7 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.