# CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG

arXiv: https://arxiv.org/abs/2010.00104 · HTML: https://mlsanity.com/paper/2010.00104/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 30 of 34)

- [Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation](https://mlsanity.com/paper/2308.13568/) (2023)
- [Wearable Accelerometer Foundation Models for Health via Knowledge Distillation](https://mlsanity.com/paper/2412.11276/) (2024)
- [WildPPG: A Real-World PPG Dataset of Long Continuous Recordings](https://mlsanity.com/paper/2412.17540/) (2024)
- [Performer: A Novel PPG-to-ECG Reconstruction Transformer for a Digital Biomarker of Cardiovascular Disease Detection](https://mlsanity.com/paper/2204.11795/) (2022)
- [Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach](https://mlsanity.com/paper/2102.12245/) (2021)
- [A Scoping Review of Deep Learning Methods for Photoplethysmography Data](https://mlsanity.com/paper/2401.12783/) (2024)
- [BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation](https://mlsanity.com/paper/2306.07730/) (2023)
- [Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models](https://mlsanity.com/paper/2405.11566/) (2024)
- [Deep Generative Models for Physiological Signals: A Systematic Literature Review](https://mlsanity.com/paper/2307.06162/) (2023)
- [Versatile Cardiovascular Signal Generation with a Unified Diffusion Transformer](https://mlsanity.com/paper/2505.22306/) (2025)
- [PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection](https://mlsanity.com/paper/2509.19774/) (2025)
- [Robust and Energy-efficient PPG-based Heart-Rate Monitoring](https://mlsanity.com/paper/2203.16339/) (2022)
- [Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning](https://mlsanity.com/paper/2605.00973/) (2026)
- [PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling](https://mlsanity.com/paper/2309.15375/) (2023)
- [ECGAN: Self-supervised generative adversarial network for electrocardiography](https://mlsanity.com/paper/2301.09496/) (2023)
- [f-GAN: A frequency-domain-constrained generative adversarial network for PPG to ECG synthesis](https://mlsanity.com/paper/2406.16896/) (2024)
- [CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals](https://mlsanity.com/paper/2502.17536/) (2025)
- [Auto-FEDUS: Autoregressive Generative Modeling of Doppler Ultrasound Signals from Fetal Electrocardiograms](https://mlsanity.com/paper/2504.13233/) (2025)
- [On Improving PPG-Based Sleep Staging: A Pilot Study](https://mlsanity.com/paper/2508.02689/) (2025)
- [Lost in Time? A Meta-Learning Framework for Time-Shift-Tolerant Physiological Signal Transformation](https://mlsanity.com/paper/2511.21500/) (2025)
- [A Comprehensive Benchmark for Electrocardiogram Time-Series](https://mlsanity.com/paper/2507.14206/) (2025)
- [BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals](https://mlsanity.com/paper/2510.02276/) (2025)
- [Temporal Cardiovascular Dynamics for Improved PPG-Based Heart Rate Estimation](https://mlsanity.com/paper/2510.27297/) (2025)
- [Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Sign](https://mlsanity.com/paper/2605.16452/) (2026)
- [P2E-VQ: ECG-linked representation augmentation for PPG via discrete patch retrieval](https://mlsanity.com/paper/2608.14656/) (2026)
- [HDformer: A Higher Dimensional Transformer for Diabetes Detection Utilizing Long Range Vascular Signals](https://mlsanity.com/paper/2303.11340/) (2023)
- [Constraint Latent Space Matters: An Anti-anomalous Waveform Transformation Solution from Photoplethysmography to Arterial Blood Pressure](https://mlsanity.com/paper/2402.17780/) (2024)
- [Joint Attention Mechanism Learning to Facilitate Opto-physiological Monitoring during Physical Activity](https://mlsanity.com/paper/2502.09291/) (2025)
- [TAU: Modeling Temporal Consistency Through Temporal Attentive U-Net for PPG Peak Detection](https://mlsanity.com/paper/2503.10733/) (2025)
- [Beyond Single-Channel: Multichannel Signal Imaging for PPG-to-ECG Reconstruction with Vision Transformers](https://mlsanity.com/paper/2505.21767/) (2025)

## Abstract (arXiv)

Electrocardiogram (ECG) is the electrical measurement of cardiac activity, whereas Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood circulation. While both signals are used for heart rate monitoring, from a medical perspective, ECG is more useful as it carries additional cardiac information. Despite many attempts toward incorporating ECG sensing in smartwatches or similar wearable devices for continuous and reliable cardiac monitoring, PPG sensors are the main feasible sensing solution available. In order to tackle this problem, we propose CardioGAN, an adversarial model which takes PPG as input and generates ECG as output. The proposed network utilizes an attention-based generator to learn local salient features, as well as dual discriminators to preserve the integrity of generated data in both time and frequency domains. Our experiments show that the ECG generated by CardioGAN provides more reliable heart rate measurements compared to the original input PPG, reducing the error from 9.74 beats per minute (measured from the PPG) to 2.89 (measured from the generated ECG).