← Back to all news
arXiv Machine Learning By P. Harris, C. Bench, M. Rinkevi\v{c}ius, V. Marozas, L. Coquelin, A. Thompson, M. Nandi, U. Hackstein, P. J. Aston

Good Practice Guide for quantifying uncertainties for machine learning models applied to photoplethysmography signals

Read the original on arXiv Machine Learning →

arXiv:2607. 19999v1 Announce Type: new Abstract: This Good Practice Guide presents work done in the QUMPHY project (Uncertainty quantification for machine learning models applied to photoplethysmography signals) that considered both machine learning and uncertainty quantification for problems which used photoplethysmography (PPG) signals from wearable devices as input.

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 Machine Learning.

Related stories

arXiv Machine Learning
Sep 4

Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review

The review examines how Uncertainty Quantification (UQ) can enhance machine learning applied to biosignals such as EEG, ECG, EOG, and EMG. It surveys 53 papers, outlining current methods, shortcomings, and theoretical frameworks, while highlighting misconceptions and gaps in diagnostic and prosthetic control contexts. The authors recommend further research on human-system interaction with UQ models in clinical settings.

By Ivo Pascal de Jong, Andreea Ioana Sburlea, Matias Valdenegro-Toro
safety
More like this →
arXiv Machine Learning
Sep 23

Robust Photoplethysmography Signal Denoising via Mamba Networks

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
More like this →
arXiv Machine Learning
Aug 20

Fuzzy Accuracy Compensates for Label Subjectivity in Classification of Skin Tone Using Wearable Photoplethysmography Signals

The study investigates classifying Fitzpatrick skin tones from wearable photoplethysmography (PPG) signals, noting that traditional accuracy is low (40‑55 %) due to subjective labeling. By introducing a fuzzy accuracy metric—treating predictions within one class of the label as correct—accuracy rises dramatically, reaching up to 96 % on tree‑based models and 85‑87 % on deep learning and feature‑based approaches. The results suggest that PPG signals contain discernible skin‑tone information, especially when evaluated with the fuzzy metric.

By Padmini Krishnadas, Urs Hackstein, Alen Bosnjakovic, Philip J. Aston
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 →