The paper introduces TF‑IG, a compact two‑branch model that learns heart‑rate from photoplethysmography by combining temporal pulse morphology and frequency‑domain periodicity. A novel interaction‑gated fusion module explicitly models agreement and discrepancy between the two domains, while a shared regression head maps the fused features to HR predictions. On the BIDMC PPG dataset, TF‑IG achieves a mean absolute error of 1.227 BPM and 92.28 % of estimates within 3 BPM using only 84.6 K parameters.
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.
The Cross-View Attention Fusion Network (CVAF‑Net) is a dual‑view deep learning model that estimates cardiac output from short photoplethysmography (PPG) segments by combining raw PPG signals with a structured prior‑guided feature sequence map through cross‑view attention. Evaluated on simulated, vasoconstriction, and real‑world activity datasets, CVAF‑Net achieved a mean absolute error of 0.19 L/min on simulated data and as low as 1.20 L/min in real settings, outperforming most benchmarks while reducing computational cost by twelvefold compared to a leading Transformer model. Plausibility checks confirmed physiologically consistent correlations with age, heart rate, and systemic vascular resistance.
Wrist-worn photoplethysmography (PPG) enables continuous monitoring of cardiopulmonary physiology, but reliable heart rate (HR) and respiratory rate (RR) estimation in free-living conditions remains challenging due to non-stationary motion artifacts that spectrally overlap with physiological dynamics. Existing signal-processing methods degrade under strong motion, while unconstrained deep learning approaches often lack physiological interpretability and identifiable structure.
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.
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.
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:2606. 30156v1 Announce Type: cross Abstract: Wrist-worn photoplethysmography (PPG) enables continuous monitoring of cardiopulmonary physiology, but reliable heart rate (HR) and respiratory rate (RR) estimation in free-living conditions remains challenging due to non-stationary motion artifacts that spectrally overlap with physiological dynamics.
arXiv:2607. 07996v1 Announce Type: cross Abstract: Continuous oxygen saturation (SpO$_2$) estimation from wearable photoplethysmography (PPG) is important for long-term health monitoring, but low-quality red and infrared PPG segments can distort waveform morphology and degrade SpO$_2$ prediction accuracy.
The paper introduces DPNet, a Mamba-based deep learning framework for denoising photoplethysmography (PPG) signals while preserving physiological information. It incorporates a scale‑invariant signal‑to‑distortion ratio loss and an auxiliary heart‑rate predictor to enhance waveform fidelity and maintain heart‑rate accuracy. Experiments on the BIDMC dataset show that DPNet outperforms conventional filtering and existing neural models in robustness against synthetic noise and real‑world motion artifacts, making it suitable for wearable healthcare systems.
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...
The paper introduces PI-AMFM, a permutation‑invariant neural framework that learns to decompose biomedical signals into variable‑cardinality amplitude‑ and frequency‑modulated (AM‑FM) modes. It combines a multiscale temporal encoder, a Mamba backbone, and component‑presence estimation, using Hungarian matching during training to handle unknown numbers of components. Experiments on synthetic AM‑FM signals and photoplethysmographic recordings show that PI‑AMFM outperforms existing methods in decomposition accuracy and successfully captures cardiac and respiratory dynamics without prior knowledge of mode count.