Synthetic Seismocardiography Signal Generation by a Generative Adversarial Network
Bibliographic record
Abstract
Aims: Seismocardiography (SCG) signals provide valuable information about the heart's performance.The technique subsists in a noisy environment where deep learning is often needed to extract important information, requiring large amounts of training data which can be expensive to obtain.In this work, we aim to create synthetic SCG heartbeats that are realistic and diverse to affordably augment current SCG datasets.Methods: We trained a Generative Adversarial Network (GAN) on real SCG heartbeats to produce synthetic SCG data.The architecture consisted of a deep convolutional GAN that was conditioned on an embedded identifier label for each subject to enable the generation of subject-specific heartbeats.Results: Our results demonstrated that the GAN could generate SCG heartbeats that closely resembled real SCG morphology.Generated heartbeats had an average root-mean-squared-error of 0.1831 when compared to the ensemble average of their real counterparts.Conclusion: The study presented a novel approach of using GANs to generate artificial SCG heartbeats.The use of GAN-generated SCG heartbeats has the potential to overcome the limitations of real SCG data availability, allowing for enhanced research and clinical applications of this valuable cardiac diagnostic technique.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".