ArticleBioengineering (Basel, Switzerland)2023
PPG2ECGps: An End-to-End Subject-Specific Deep Neural Network Model for Electrocardiogram Reconstruction from Photoplethysmography Signals without Pulse Arrival Time Adjustments.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- P2Es: Physiology-Conditioned Diffusion for 12-Lead ECG Generation from PPG.Smart health (Amsterdam, Netherlands) · 2026Article
- A multitask Transformer-VAE framework for robust PPG denoising and atrial fibrillation screening.BMC medical imaging · 2026Article
- A Physics-Aware Diffusion Framework for Robust ECG Synthesis Using Mesoscopic Lattice Boltzmann Constraints.Biology · 2026Article
- Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention.Biomedicines · 2025Review
- Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals.Sensors (Basel, Switzerland) · 2025Article
- Biomedical Data Mining and Machine Learning for Disease Diagnosis and Health Informatics.Bioengineering (Basel, Switzerland) · 2024Article
- Reconstruction of Missing Electrocardiography Signals from Photoplethysmography Data Using Deep Neural Network.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Electrocardiograms (ECGs) provide crucial information for evaluating a patient's cardiovascular health; however, they are not always easily accessible. Photoplethysmography (PPG), a technology commonly used in wearable devices such as smartwatches, has shown promise for constructing ECGs. Several methods have been proposed for ECG reconstruction using PPG signals, but some require signal alignment during the training phase, which is not feasible in real-life settings where ECG signals are not collected at the same time as PPG signals. To address this challenge, we introduce PPG2ECGps, an end-to-end, patient-specific deep-learning neural network utilizing the W-Net architecture. This novel approach enables direct ECG signal reconstruction from PPG signals, eliminating the need for signal alignment. Our experiments show that the proposed model achieves mean values of 0.977 mV for Pearson's correlation coefficient, 0.037 mV for the root mean square error, and 0.010 mV for the normalized dynamic time-warped distance when comparing reconstructed ECGs to reference ECGs from a dataset of 500 records. As PPG signals are more accessible than ECG signals, our proposed model has significant potential to improve patient monitoring and diagnosis in healthcare settings via wearable devices.
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Registered trials
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