# An End-to-End and Accurate PPG-based Respiratory Rate Estimation Approach Using Cycle Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2105.00594 · HTML: https://mlsanity.com/paper/2105.00594/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 6 of 6)

- [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)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://mlsanity.com/paper/1604.04382/) (2016)
- [Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS](https://mlsanity.com/paper/2011.10470/) (2020)

## Built on by (most cited first, 11 of 11)

- [An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN](https://mlsanity.com/paper/2106.11512/) (2021)
- [RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation](https://mlsanity.com/paper/2208.08672/) (2022)
- [RespDiff: An End-to-End Multi-scale RNN Diffusion Model for Respiratory Waveform Estimation from PPG Signals](https://mlsanity.com/paper/2410.04366/) (2024)
- [Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks](https://mlsanity.com/paper/2201.09976/) (2022)
- [Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones](https://mlsanity.com/paper/2204.08989/) (2022)
- [PENGUIN: General Vital Sign Reconstruction from PPG with Flow Matching State Space Model](https://mlsanity.com/paper/2602.03858/) (2026)
- [Blind Source Separation of Single-Channel Mixtures via Multi-Encoder Autoencoders](https://mlsanity.com/paper/2309.07138/) (2023)
- [Robust CNN-based Respiration Rate Estimation for Smartwatch PPG and IMU](https://mlsanity.com/paper/2401.05469/) (2024)
- [Rapid Extraction of Respiratory Waveforms from Photoplethysmography: A Deep Encoder Approach](https://mlsanity.com/paper/2212.12578/) (2022)
- [VAMP-Diff: VampPrior Latent Diffusion for Photoplethysmography Modeling](https://mlsanity.com/paper/2605.22851/) (2026)
- [Learning Where to Look: A Shared Relative-Alignment Module for Time-Series Forecasting and PPG-to-Vital-Sign Reconstruction](https://mlsanity.com/paper/2609.27473/) (2026)

## Abstract (arXiv)

Respiratory rate (RR) is a clinical sign representing ventilation. An abnormal change in RR is often the first sign of health deterioration as the body attempts to maintain oxygen delivery to its tissues. There has been a growing interest in remotely monitoring of RR in everyday settings which has made photoplethysmography (PPG) monitoring wearable devices an attractive choice. PPG signals are useful sources for RR extraction due to the presence of respiration-induced modulations in them. The existing PPG-based RR estimation methods mainly rely on hand-crafted rules and manual parameters tuning. An end-to-end deep learning approach was recently proposed, however, despite its automatic nature, the performance of this method is not ideal using the real world data. In this paper, we present an end-to-end and accurate pipeline for RR estimation using Cycle Generative Adversarial Networks (CycleGAN) to reconstruct respiratory signals from raw PPG signals. Our results demonstrate a higher RR estimation accuracy of up to 2× (mean absolute error of 1.9±0.3 using five fold cross validation) compared to the state-of-th-art using a identical publicly available dataset. Our results suggest that CycleGAN can be a valuable method for RR estimation from raw PPG signals.