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.
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.
By I Chiu, Yu-Tung Liu, Kuan-Chen Wang, Hung-Yu Wei, Yu Tsao
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. 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.
By Kindeep K. Dhatt, Tengyue Wu, Hanbang Hua, Yayun Du
The study investigates how adding ballistocardiography (BCG) signals to photoplethysmography (PPG) and arterial pressure wave (APW) signals affects the estimation of cardiovascular biomarkers. Using a unified whole‑body cardiovascular model, synthetic PPG, APW, and BCG signals were generated and analyzed with neural posterior estimation and simulation‑based inference. Results show that incorporating BCG significantly improves estimation accuracy, converting multimodal posterior distributions from PPG/APW alone into unimodal distributions, thereby enhancing the reliability of cardiovascular biomarker estimation.
By Shusaku Maeda, Masahiro Nakano, Tomoharu Iwata, Kenji Komiya, Ryo Nishikimi, Kunio Kashino
Remote photoplethysmography (rPPG) estimates the blood volume pulse (BVP) signal from facial videos, enabling contact-free health monitoring. Conventional clip-wise approaches, which use video clips as input, require capturing over one hundred frames before inference, thus introducing several seconds of delay and hindering real-time use.
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.
By Bo Wu, Haoling Wang, Zhuodiao Kuang, Kateryna Shapovalenko
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.
By Eloy Geenjaar, Vince Calhoun, Scott Daly, Gouthaman KV, Lie Lu, Trisha Mittal, Daniel P. Darcy
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.
By Yuanyuan Zhang, Yida Zhang, Jiahui Li, Yuyan Wu, Fei Dou, Xiao Yin, Zhenlin An, Hae Young Noh, Wenzhan Song
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.
By Yidan Shen, Neville Mathew, Maham Rahimi, Deependra Dhakal, George Zouridakis, Xin Fu, Renjie Hu
arXiv:2608. 05697v1 Announce Type: cross Abstract: Respiration provides a continuously available window into physiological state and behavior.
By Mohammad Hosseini, Hamed Khatounabadi, Mohammad Fakharzadeh
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.
By Farouk Ganiyu Adewumi, Timothy Oladunni, Rochak Ghimire, Kosisochukwu Ogbuanya, Sanaa Reeves, Sandy Akoy