← Back to all news
arXiv Machine Learning By Kindeep K. Dhatt, Tengyue Wu, Hanbang Hua, Yayun Du

Single-Beat Cuffless Blood Pressure Estimation Using Ear-PPG and ECG with a Lightweight Hybrid Learning Framework

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Jun 10

DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals

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
llms
More like this →
Hugging Face Trending Papers
Jun 29

Physically-Constrained Harmonic Separation for Robust Heart and Respiratory Rate Estimation from Wrist Photoplethysmography

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.

benchmarkssafety
More like this →
arXiv Machine Learning
Jul 28

On-Device Inference versus Wireless Streaming: Energy-Efficient Multi-Modal Deep Learning for Wearable Cardiovascular Patches

arXiv:2510. 18668v4 Announce Type: replace Abstract: Wearable cardiovascular sensor patches promise continuous, unobtrusive monitoring, but their tight energy, memory, and compute budgets make it unclear whether physiological signals should be analyzed on the device or streamed to the cloud for processing.

By Mustafa Fuad Rifet Ibrahim, Tunc Alkanat, Felix Manthey, Maurice Meijer, Alexander Schlaefer, Peer Stelldinger
multimodalbenchmarks
More like this →