Beat-by-Beat ECG Monitoring from Photoplythmography Based on Scattering Wavelet Transform
Bibliographic record
Abstract
The electrocardiogram (ECG) is a popular measurement scheme to assess and diagnose cardiovascular diseases.ECG devices use gel material and electrodes that may cause skin irritation and discomfort during long use, which restricts the long-term use of these devices.On the other hand, Photoplethysmography (PPG) is an optical approach used to estimate the skin blood flow using photons.Recently, the relationship between the PPG and ECG has been recognized and there are early stage attempts to reconstruct the ECG signals from PPG signals that can lead to giving up electrodes and skin irritating and uncomfortable materials.However, these recent researches suffer from the sensitivity to the PPG signal quality, shifting, and scaling.Therefore, it is restricted with constrained PPG signals.In this paper, we propose an ECG reconstruction system that is independent of PPG scaling and shifting.The proposed system is based on scattering wavelet transform (SWT) as a feature domain along with the deep learning network.Using SWT helps deep learning networks to learn the non-linear relationship between ECG and PPG even with small datasets.Also, the proposed system is based on beat-by-beat ECG estimation rather than signal-based which leads to the learning of local features rather than global features.Based on the presented simulation results, the proposed system with beat-by-beat SWT features extraction outperforms the other feature domains; Time, DCT, and DWT domains.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".