arXiv:2608. 07759v1 Announce Type: cross Abstract: Cardiovascular AI models can classify clean elec- trocardiogram (ECG) signals, but real wearable signals change because of motion, breathing, posture, sensor contact, and true clinical deterioration.
arXiv:2607. 27076v1 Announce Type: new Abstract: Continuous cuffless blood pressure (BP) monitoring remains challenging due to motion artifacts, physiological variability, and the limited robustness of conventional pulse transit time (PTT) models under dynamic conditions.
arXiv:2607. 23406v1 Announce Type: cross Abstract: Continuous cuffless blood pressure (BP) monitoring is essential for connected health systems and wearable devices, enabling early detection, longitudinal tracking, and personalized management of cardiovascular disease.
The study investigates how adding ballistocardiography (BCG) signals to photoplethysmography (PPG) and arterial pressure wave (APW) signals affects the estimation of cardiovascular biomarkers. Using a unified whole‑body cardiovascular model, synthetic PPG, APW, and BCG signals were generated and analyzed with neural posterior estimation and simulation‑based inference. Results show that incorporating BCG significantly improves estimation accuracy, converting multimodal posterior distributions from PPG/APW alone into unimodal distributions, thereby enhancing the reliability of cardiovascular biomarker estimation.
CardioFusion-AI is a framework for robust fusion of ECG and PPG signals in wearable physiological monitoring. Its signal‑processing front end, featuring R‑peak and systolic‑peak detection, an Orphanidou‑type signal‑quality index, and beat‑by‑beat pulse transit time estimation, was validated on 53 intensive‑care recordings and a fetal ECG database. In a controlled synthetic degradation study, attention fusion achieved the lowest overall error, while adaptive gates reallocated weight toward the healthy modality under complete loss, and signal‑quality conditioning improved performance when PPG was missing.
Wearable electrocardiogram (ECG) and photoplethysmogram (PPG) sensors are complementary but individually fragile: motion artifact, poor contact, and sensor dropout can degrade one or both signals. Fus...
arXiv:2608. 12944v1 Announce Type: new Abstract: Electrocardiography (ECG), photoplethysmography (PPG), and phonocardiography (PCG) provide complementary views of the same cardiac cycle, yet existing cardiac foundation models are trained for a single sensing modality, leaving the shared physiology across sensors unexploited.
arXiv:2607. 23412v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences.
arXiv:2608. 03597v1 Announce Type: new Abstract: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased risks of stroke, heart failure, and mortality.
arXiv:2607. 20027v1 Announce Type: new Abstract: Short-term Heart Rate Variability (HRV) forecasting could provide clinicians with actionable lead time for detecting autonomic dysfunction and adverse cardiac events.
Deep learning models for electrocardiogram (ECG) classification often suffer from significant performance degradation when deployed in unseen domains due to shifts in acquisition devices and patient p...
arXiv:2606. 07692v1 Announce Type: cross Abstract: Foundation models for wearable biosignals have matched or exceeded supervised specialists across a range of clinical tasks, yet all rely on modalities that require deliberate user action--wearing a device or visiting a sleep lab.
The study investigates ECG biometrics by training a Siamese ResNet with late multi-lead fusion on a large dataset from cardiopulmonary exercise tests. It evaluates the model under realistic conditions, including exercise-induced stress and cross-session variability, achieving an intra-session rest-to-peak EER of 1.7% and a state‑of‑the‑art 3.9% on the CYBHi dataset. The results demonstrate that an intrinsic cardiac signature remains robust to physiological and temporal drift.