The paper presents a hybrid Transformer framework that estimates non‑invasive continuous blood pressure from ECG/PPG‑derived feature sequences. It models 10‑step sequences of six physiological descriptors and two demographic covariates, combining Transformer, Kolmogorov‑Arnold Network, and XGBoost modules, and uses a dynamic fusion decoder to predict diastolic and systolic BP. On a large MIMIC‑III dataset, the model achieved mean errors of 0.41 mmHg (diastolic) and –1.60 mmHg (systolic) with high accuracy within 10 mmHg for most predictions.
arXiv:2606. 11125v1 Announce Type: cross Abstract: Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cost, wearable-friendly cuffless BP estimation.
arXiv:2605.10871v3 Announce Type: replace-cross Abstract: We present Attractor-Vascular Coupling Theory (AVCT), a theory-guided framework for deriving interpretable blood pressure (BP) predictors fro...
arXiv:2609.13190v1 Announce Type: new Abstract: Continuous cuffless blood pressure (BP) monitoring using photoplethysmography (PPG) offers a promising solution for personalized healthcare. However, e...
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.
SIFPBPNet is a dual‑path neural network designed for cuffless blood‑pressure estimation from photoplethysmography (PPG) signals. It separates steady‑state and instantaneous features through a Steady‑state Feature Path (SFP) that uses a Graph Attention Network to learn individual‑specific long‑term patterns, and an Instantaneous Feature Path (IFP) that captures short‑term dynamics and fuses them with the steady‑state prior via cross‑attention. On a large wearable dataset, the model achieves mean absolute errors of 8.57 mmHg for systolic and 5.97 mmHg for diastolic BP, outperforming existing methods and showing that the SFP module can be added to other backbones for 2.8–13.1 % MAE reduction.
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 evaluates how acquisition context and photoplethysmography (PPG) signals contribute to session‑calibrated blood pressure tracking using a wearable‑state model on 653 AuroraBP participants. Adding contextual information reduced mean absolute errors (MAEs) for systolic and diastolic BP by 0.436 mmHg and 0.126 mmHg, respectively, while PPG alone provided modest improvements. Exploratory follow‑ups with sensor‑history ensembles and pulse‑rate fusion yielded only slight gains, and a separate intraoperative model (VitalDB) showed limited PPG benefit.
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.
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.
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.
This study develops a rolling day‑wise mortality prediction model for critically ill patients with acute kidney injury on continuous renal replacement therapy (CRRT) by incorporating minute‑level machine pressure waveforms alongside electronic health record (EHR) data. After cleaning the raw machine data—aligning it with therapy intervals, removing priming and downtime minutes, and denoising artifacts—the authors trained a transformer‑based stacked ensemble that fuses a sequence transformer with classical models using circuit‑instability features and clinical variables. In a multi‑center benchmark of 976 patients, the combined model achieved a one‑day mortality AUROC of 0.766, outperforming machine‑only (0.625) and EHR‑only (0.717) models, and SHAP analysis highlighted key pressure‑related features such as filter pressure, transmembrane pressure, and access‑to‑return difference. whyItMatters":"By integrating previously discarded CRRT machine data, the study provides a continuous, real‑time risk signal that could enable earlier detection of patient deterioration and improve mortality prediction in a high‑risk population."
arXiv:2607. 05009v1 Announce Type: cross Abstract: Complete digital 12-lead electrocardiograms (ECGs) are essential for AI-enabled cardiovascular assessment, yet many clinical ECG records, particularly those digitized from ECG images, remain incomplete because of short display formats, incomplete waveform digitization, lead loss, or signal corruption.