Inception CNN-Transformer for Robust PPG-to-ECG Reconstruction
Bibliographic record
Abstract
In recent years, wearable healthcare devices and robots have become increasingly crucial in the face of population aging and the rising prevalence of chronic diseases, including cardiovascular disease, which remains a leading cause of mortality worldwide. These conditions amplified the demand for advanced, continuous monitoring of cardiovascular health. While electrocardiogram (ECG) signals offer comprehensive diagnostic insights, their reliance on multiple electrodes often limits practicality. In contrast, photoplethysmogram (PPG) signals are more convenient to acquire but lack the rich detail of ECG. In this paper, we propose a novel PPG-to-ECG reconstruction method that combines an Inception CNN for multiscale feature extraction with a Transformer architecture for capturing global dependencies. Our proposed method achieves robust ECG signal reconstruction even under high-noise conditions by effectively preserving local morphological details and leveraging long-range contextual information. We validate the proposed approach on diverse datasets spanning everyday life and intensive care unit (ICU) settings, demonstrating high accuracy and generalizability. Experimental results indicate an RMSE of 0.26, corresponding to a 10% improvement over state-of-the-art methods. These findings highlight the feasibility of reliable, real-time ECG reconstruction from PPG signals alone, paving the way for scalable and accessible healthcare monitoring solutions in clinical and wearable contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".