# Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2201.09976 · HTML: https://mlsanity.com/paper/2201.09976/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [An End-to-End and Accurate PPG-based Respiratory Rate Estimation Approach Using Cycle Generative Adversarial Networks](https://mlsanity.com/paper/2105.00594/) (2021)
- [An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN](https://mlsanity.com/paper/2106.11512/) (2021)
- [Cuffless Blood Pressure Estimation from Electrocardiogram and Photoplethysmogram Using Waveform Based ANN-LSTM Network](https://mlsanity.com/paper/1811.02214/) (2018)
- [A Deep Learning Approach to Predict Blood Pressure from PPG Signals](https://mlsanity.com/paper/2108.00099/) (2021)
- [Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS](https://mlsanity.com/paper/2011.10470/) (2020)
- [Energy-efficient Blood Pressure Monitoring based on Single-site Photoplethysmogram on Wearable Devices](https://mlsanity.com/paper/2108.00672/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Scoping Review of Deep Learning Methods for Photoplethysmography Data](https://mlsanity.com/paper/2401.12783/) (2024)
- [INN-PAR: Invertible Neural Network for PPG to ABP Reconstruction](https://mlsanity.com/paper/2409.09021/) (2024)

## Abstract (arXiv)

Continuous monitoring of blood pressure (BP)can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions. Recent approaches fuse Photoplethysmograph (PPG) and electrocardiographic (ECG) signals using different machine and deep learning approaches to non-invasively estimate BP; however, they fail to reconstruct the complete signal, leading to less accurate models. In this paper, we propose a cycle generative adversarial network (CycleGAN) based approach to extract a BP signal known as ambulatory blood pressure (ABP) from a clean PPG signal. Our approach uses a cycle generative adversarial network that extends theGAN architecture for domain translation, and outperforms state-of-the-art approaches by up to 2x in BP estimation.