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
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
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. 15927v1 Announce Type: new Abstract: Diabetes and extreme blood sugar levels are some of the major health problems faced by humans today across the world.
By Ruhani Bhatia, Vijval Ekbote
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
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.
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
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 paper introduces a deep learning-based signal quality assessment model that differentiates clean from noisy ambulatory ECG recordings. It is trained on the Copenhagen Center for Health Technology-Contextualized Arrhythmia Database, the first ambulatory ECG database that includes both physical and patient-reported contextual data. The model maintains stable performance across other datasets such as MIT and PhysioNet/CinC Challenge 2021, and the study demonstrates how the model can be used to investigate complex ECG noise in conjunction with contextual information.
By Xiaopeng Mao, Marike Weisbjerg, Sadasivan Puthusserypady
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
arXiv:2606. 30156v1 Announce Type: cross Abstract: 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.
By Nouhaila Fraihi, Ouassim Karrakchou, Mounir Ghogho
arXiv:2504. 18433v3 Announce Type: replace Abstract: Uncertainty quantification is crucial in machine learning, yet most (axiomatic) studies of uncertainty measures focus on classification, leaving a gap in regression settings with limited formal justification and evaluations.
By Christopher B\"ulte, Yusuf Sale, Timo L\"ohr, Paul Hofman, Gitta Kutyniok, Eyke H\"ullermeier