# PPG2ABP: Translating Photoplethysmogram (PPG) Signals to Arterial Blood Pressure (ABP) Waveforms using Fully Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2005.01669 · HTML: https://mlsanity.com/paper/2005.01669/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 18 of 18)

- [Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation](https://mlsanity.com/paper/2308.13568/) (2023)
- [Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach](https://mlsanity.com/paper/2102.12245/) (2021)
- [Versatile Cardiovascular Signal Generation with a Unified Diffusion Transformer](https://mlsanity.com/paper/2505.22306/) (2025)
- [Cuff-less Arterial Blood Pressure Waveform Synthesis from Single-site PPG using Transformer & Frequency-domain Learning](https://mlsanity.com/paper/2401.05452/) (2024)
- ["Can't Take the Pressure?": Examining the Challenges of Blood Pressure Estimation via Pulse Wave Analysis](https://mlsanity.com/paper/2304.14916/) (2023)
- [BP-DeepONet: A new method for cuffless blood pressure estimation using the physcis-informed DeepONet](https://mlsanity.com/paper/2402.18886/) (2024)
- [TransfoRhythm: A Transformer Architecture Conductive to Blood Pressure Estimation via Solo PPG Signal Capturing](https://mlsanity.com/paper/2404.15352/) (2024)
- [Auto-FEDUS: Autoregressive Generative Modeling of Doppler Ultrasound Signals from Fetal Electrocardiograms](https://mlsanity.com/paper/2504.13233/) (2025)
- [Lost in Time? A Meta-Learning Framework for Time-Shift-Tolerant Physiological Signal Transformation](https://mlsanity.com/paper/2511.21500/) (2025)
- [Signal or Noise? Understanding Generative Models for Real-World Sensor Time Series](https://mlsanity.com/paper/2607.04245/) (2026)
- [ArterialNet: Reconstructing Arterial Blood Pressure Waveform with Wearable Pulsatile Signals, a Cohort-Aware Approach](https://mlsanity.com/paper/2410.18895/) (2024)
- [Constraint Latent Space Matters: An Anti-anomalous Waveform Transformation Solution from Photoplethysmography to Arterial Blood Pressure](https://mlsanity.com/paper/2402.17780/) (2024)
- [MD-ViSCo: A Unified Model for Multi-Directional Vital Sign Waveform Conversion](https://mlsanity.com/paper/2506.08357/) (2025)
- [Collaborative Learning-Enhanced Lightweight Models for Predicting Arterial Blood Pressure Waveform in a Large-scale Perioperative Dataset](https://mlsanity.com/paper/2508.11669/) (2025)
- [Physiology as Language: Translating Respiration to Sleep EEG](https://mlsanity.com/paper/2602.00526/) (2026)
- [HIPNO: Symmetry-Aware Physics-Informed Neural Operators for Noninvasive Hemodynamic Inference](https://mlsanity.com/paper/2608.10011/) (2026)
- [A Multidimensional Data-Driven Hybrid Transformer Framework for Non-invasive Continuous Blood Pressure Prediction](https://mlsanity.com/paper/2608.23276/) (2026)
- [Impact of Multiple Non-Invasive Biosignals on Cardiovascular Biomarker Estimation via Simulation-Based Inference](https://mlsanity.com/paper/2609.11969/) (2026)

## Abstract (arXiv)

Cardiovascular diseases are one of the most severe causes of mortality, taking a heavy toll of lives annually throughout the world. The continuous monitoring of blood pressure seems to be the most viable option, but this demands an invasive process, bringing about several layers of complexities. This motivates us to develop a method to predict the continuous arterial blood pressure (ABP) waveform through a non-invasive approach using photoplethysmogram (PPG) signals. In addition we explore the advantage of deep learning as it would free us from sticking to ideally shaped PPG signals only, by making handcrafted feature computation irrelevant, which is a shortcoming of the existing approaches. Thus, we present, PPG2ABP, a deep learning based method, that manages to predict the continuous ABP waveform from the input PPG signal, with a mean absolute error of 4.604 mmHg, preserving the shape, magnitude and phase in unison. However, the more astounding success of PPG2ABP turns out to be that the computed values of DBP, MAP and SBP from the predicted ABP waveform outperforms the existing works under several metrics, despite that PPG2ABP is not explicitly trained to do so.