# Performer: A Novel PPG-to-ECG Reconstruction Transformer for a Digital Biomarker of Cardiovascular Disease Detection

arXiv: https://arxiv.org/abs/2204.11795 · HTML: https://mlsanity.com/paper/2204.11795/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG](https://mlsanity.com/paper/2010.00104/) (2020)

## Built on by (most cited first, 13 of 13)

- [Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation](https://mlsanity.com/paper/2308.13568/) (2023)
- [Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models](https://mlsanity.com/paper/2405.11566/) (2024)
- [PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection](https://mlsanity.com/paper/2509.19774/) (2025)
- [CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals](https://mlsanity.com/paper/2502.17536/) (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)
- [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)
- [An AI-Enabled Framework Within Reach for Enhancing Healthcare Sustainability and Fairness](https://mlsanity.com/paper/2406.07558/) (2024)
- [Beyond Single-Channel: Multichannel Signal Imaging for PPG-to-ECG Reconstruction with Vision Transformers](https://mlsanity.com/paper/2505.21767/) (2025)
- [Physiology as Language: Translating Respiration to Sleep EEG](https://mlsanity.com/paper/2602.00526/) (2026)
- [PG-LRF: Physiology-Guided Latent Rectified Flow for Electro-Hemodynamic PPG-to-ECG Generation](https://mlsanity.com/paper/2605.12541/) (2026)
- [Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals](https://mlsanity.com/paper/2605.18483/) (2026)

## Abstract (arXiv)

Electrocardiography (ECG), an electrical measurement which captures cardiac activities, is the gold standard for diagnosing cardiovascular disease (CVD). However, ECG is infeasible for continuous cardiac monitoring due to its requirement for user participation. By contrast, photoplethysmography (PPG) provides easy-to-collect data, but its limited accuracy constrains its clinical usage. To combine the advantages of both signals, recent studies incorporate various deep learning techniques for the reconstruction of PPG signals to ECG; however, the lack of contextual information as well as the limited abilities to denoise biomedical signals ultimately constrain model performance. In this research, we propose Performer, a novel Transformer-based architecture that reconstructs ECG from PPG and combines the PPG and reconstructed ECG as multiple modalities for CVD detection. This method is the first time that Transformer sequence-to-sequence translation has been performed on biomedical waveform reconstruction, combining the advantages of both PPG and ECG. We also create Shifted Patch-based Attention (SPA), an effective method to encode/decode the biomedical waveforms. Through fetching the various sequence lengths and capturing cross-patch connections, SPA maximizes the signal processing for both local features and global contextual representations. The proposed architecture generates a state-of-the-art performance of 0.29 RMSE for the reconstruction of PPG to ECG on the BIDMC database, surpassing prior studies. We also evaluated this model on the MIMIC-III dataset, achieving a 95.9% accuracy in CVD detection, and on the PPG-BP dataset, achieving 75.9% accuracy in related CVD diabetes detection, indicating its generalizability. As a proof of concept, an earring wearable named PEARL (prototype), was designed to scale up the point-of-care (POC) healthcare system.