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.
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: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 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.
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. 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:2507. 12645v1 Announce Type: cross Abstract: The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring.
arXiv:2602. 04266v2 Announce Type: replace-cross Abstract: Aortic valve disease (AVD) represents a major public health burden, while its diagnosis relies on echocardiography, which is limited by cost and specialist expertise, restricting scalable screening and risk stratification.
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 presents a hybrid predictive ensemble that merges machine learning and deep neural network techniques to detect and prognosticate cardiovascular disease early. It processes real‑time physiological data from IoMT devices, applying preprocessing, feature selection, and optimized classifiers (SVM, Random Forest, XGBoost) within an ensemble architecture. The cloud‑based system achieves higher accuracy, fewer false positives, and consistent performance on real‑world datasets, supporting continuous patient monitoring and clinical decision support.
arXiv:2607. 09749v1 Announce Type: cross Abstract: Foundation models have recently emerged as a powerful paradigm for learning transferable representations from large scale biomedical data, yet existing approaches for physiological waveforms primarily optimize reconstruction or forecasting objectives that do not explicitly preserve clinically meaningful waveform morphology.
arXiv:2606. 06718v1 Announce Type: cross Abstract: Myocardial substrate abnormalities, such as myocardial scar and myocardial infarction (MI), are associated with adverse cardiovascular outcomes.