arXiv:2608. 14656v1 Announce Type: cross Abstract: Photoplethysmography (PPG) is widely used in consumer wearables because of its low cost and ease of acquisition.
BeatFlow-ECG is a conditional rectified‑flow model that reconstructs single‑channel ECG signals from synchronized photoplethysmography (PPG) and inertial measurement unit (IMU) data. The architecture uses a convolutional encoder‑decoder with a transformer bottleneck and explicit flow‑time conditioning, incorporating motion information through IMU‑derived features, motion‑dependent loss weighting, and a curriculum learning strategy. Evaluated on PPG‑DaLiA and WESAD datasets with leave‑one‑subject‑out protocols, BeatFlow‑ECG outperforms deterministic, adversarial, and diffusion‑based baselines, achieving Pearson correlations of 0.983–0.986 and R‑peak F1 scores of 0.946–0.955, while reducing L1 error compared to Conditional DDPM‑1D.
Continuous cardiac monitoring outside clinical settings requires signals that are both informative and practical to collect during daily life. Electrocardiography (ECG) provides rich information about...
arXiv:2606. 07365v1 Announce Type: cross Abstract: Photoplethysmography (PPG), a non-invasive measure of changes in blood volume, is widely used in both wearable devices and clinical settings.
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.
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: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.
PPG-Bench is an open‑source benchmark that evaluates five publicly available photoplethysmography (PPG) foundation models across nine datasets and 26 tasks, covering cardiovascular state estimation, activity, sleep, screening, and identification. The benchmark tests models under four downstream regimes—linear probing, random forests, frozen MLP probes, and end‑to‑end finetuning—while also comparing them to three controls: a random/constant baseline, a deterministic pulse‑rate‑variability feature set, and a generic time‑series foundation model (MOMENT). Results show that over a third of task‑regime combinations yield no advantage for any PPG foundation model over the baseline, and in most cases no PPG‑specific model outperforms the simple controls, indicating that current PPG foundation models lack generalised, transferable representations.
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:2605. 29977v2 Announce Type: replace-cross Abstract: High-fidelity ECG interpretation is increasingly reliant on massive foundation models, yet their deployment in clinical edge-care remains hindered by extreme computational demands.
arXiv:2608. 05893v1 Announce Type: new Abstract: Electrocardiography (ECG) is one of the most widely used non-invasive tools for diagnosing cardiovascular disease, but transforming multi-lead ECG recordings into reliable clinical reports remains challenging.