Generative Reconstruction of Multimodal Cardiac Waveforms From a Single Vibrational Cardiography Sensor

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.