Generative Reconstruction of Multimodal Cardiac Waveforms From a Single Vibrational Cardiography Sensor
Bibliographic record
Abstract
Multimodal cardiac monitoring systems require multiple sensors. This complexity renders them impractical in scenarios requiring continuous monitoring in everyday life. To overcome this limitation, we demonstrate a generative modeling framework that leverages the vibrational cardiography (VCG) signal from a single, wearable sensor to estimate multiple cardiac waveforms. We recorded VCG signals at the xiphoid process of 20 subjects, along with electrocardiography (ECG), impedance cardiography (ICG), non-invasive blood pressure (NIBP), and photoplethysmography (PPG). To broaden the range of cardiac, respiratory, and hemodynamic responses, we included breath holding, deep breathing, and the cold pressor test. A conditional Generative Adversarial Network was trained to reconstruct normalized ECG, ICG, NIBP, and PPG signals from VCG inputs. The model was evaluated using a leave-one-subject-out validation scheme to assess calibration-free generalization across individuals. We show that the reconstructed waveforms exhibit strong alignment with their respective targets, capturing both morphological structure and temporal dynamics, with median Pearson's correlation coefficients of 0.808, 0.907, 0.833, and 0.929 for ECG, NIBP, ICG, and PPG, respectively. We demonstrate that accuracy is consistent across interventions. The fiducial point analysis demonstrated the ability to capture key cardiac features within each waveform. Hence, we demonstrate the viability of combining single-sensor VCG with generative modelling to estimate multiple cardiac waveforms, offering a streamlined alternative to conventional multimodal sensor systems. This approach has the potential to improve the practicality of continuous cardiac monitoring in daily life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".