The paper introduces cylindrical geodesic flow matching, a method for translating quasiperiodic cardiovascular waveforms between different body locations. By replacing the standard affine path in flow matching with a closed‑form geodesic on a phase–amplitude cylinder, the approach preserves amplitude and instantaneous frequency during interpolation. Experiments on photoplethysmography and seismocardiography data show that this geometry‑aware method outperforms interpolation baselines and matches or exceeds supervised models, reducing error metrics by up to ~15%.
arXiv:2610.09397v1 Announce Type: new Abstract: Cine cardiovascular magnetic resonance (CMR) analysis relies on multi-frame sequences capturing the full cardiac cycle. However, standard multi-frame a...
The paper introduces a self‑supervised method for detecting end‑diastole (ED) and end‑systole (ES) in echocardiography by constraining the latent motion to a single‑parameter orbit, effectively modeling cardiac phase as a one‑dimensional signal. This approach yields an interpretable representation that directly identifies ED and ES, improving ED localisation and matching ES performance compared to prior state‑of‑the‑art methods, while using fewer training epochs and a more constrained model. The method is trained on EchoNet‑Dynamic without annotations and the code is publicly available.
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.
The paper introduces Phy‑BP, a physics‑constrained deep learning framework for contactless blood pressure monitoring using triaxial bodyseismography (BSG). It employs an adaptive quality‑control algorithm to select cardiogenic‑rich BSG segments and embeds a 3‑D wave‑propagation physical model into the neural network to align multi‑axis features, enhancing robustness to real‑world distortions. Experiments on a 162‑hour hospital dataset from 21 subjects demonstrate that Phy‑BP can filter low‑quality measurements and maintain accurate BP estimation even with limited training data.
arXiv:2609.34965v2 Announce Type: replace-cross Abstract: Many physiological time series, such as cardiac and brain recordings, exhibit cyclostationarity: their statistics vary periodically with an u...
arXiv:2610.09185v1 Announce Type: new Abstract: Cine cardiovascular magnetic resonance (CMR) captures the cardiac cycle as a four-dimensional (4D) sequence, but standard acquisition requires electroc...
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.
Remote Photoplethysmography (rPPG) enables contactless pulse estimation from facial videos, serving as a vital tool for health monitoring. However, current deep learning methods often struggle under complex disturbances, particularly varying illumination, facial expressions, and unconstrained head movements.
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.
AF-Mamba is a deep learning model that predicts atrial fibrillation (AF) onset one hour in advance using long‑term RR intervals. It combines temporal convolutional networks for local feature extraction with Mamba, a state‑space model for long‑range sequence modeling, achieving high sensitivity (0.889) and specificity (0.943) in subject‑wise testing. The model maintains strong performance across unseen datasets, offering a favorable trade‑off between predictive accuracy and computational efficiency for real‑time ambulatory monitoring.
arXiv:2608. 12695v1 Announce Type: new Abstract: Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences.
SOTER is a generative foundation model designed for wearable physiological time‑series data. It integrates cross‑channel coupling, spectrum‑guided expert specialization, and continuous‑time latent evolution, using a spatial feature‑aware backbone, a PSD‑guided mixture‑of‑experts layer, and a neural controlled differential equation decoder. Trained on 226 billion time points from five public datasets, SOTER outperforms baselines in zero‑shot forecasting, classification, and imputation across six benchmarks, and remains robust to additive noise.