arXiv AI By Farouk Ganiyu Adewumi, Timothy Oladunni

Context-Augmented, Session-Calibrated Blood Pressure Tracking from Photoplethysmography

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
Jul 30

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

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
arXiv Machine Learning
Sep 4

A Multidimensional Data-Driven Hybrid Transformer Framework for Non-invasive Continuous Blood Pressure Prediction

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.

By Yuexin Ma, Jingqi Hou, Yuxuan Kang, Zhaoying Liu
Hugging Face Trending Papers
Aug 27

Property-Specific Recoverability from Contact PPG to Camera rPPG under Heterogeneous Observation Conditions

The study examined how well specific physiological properties of contact photoplethysmography (PPG) can be recovered when converted to camera-derived remote photoplethysmography (rPPG) across 655 recordings. Using the CHROM method, the authors found that while overall heart‑rate accuracy was modest, many dynamic properties (e.g., autocorrelation, spectral measures, Lyapunov exponents) showed little recording‑specific correspondence, and differences varied with skin tone and observation conditions. The results demonstrate that preserving individual recording characteristics depends on the property and conditions, and population‑level agreement does not guarantee individual‑level fidelity.

arXiv AI
Aug 28

Property-Specific Recoverability from Contact PPG to Camera rPPG under Heterogeneous Observation Conditions

The study examined how well physiological properties from contact photoplethysmography (PPG) can be recovered by camera-based remote photoplethysmography (rPPG) across 655 recordings. Using the CHROM method, the authors found that while heart‑rate estimates showed modest accuracy, other dynamic measures such as autocorrelation, spectral content, and Lyapunov exponents largely failed to preserve recording‑specific characteristics. They also observed that lighting and motion influence endpoint accuracy, yet these factors did not explain the lack of dynamical correspondence.

By Timothy Oladunni, Farouk Ganiyu-Adewumi