3 papers
cs.LG2026
Benchmark Problems and Benchmark Datasets for the evaluation of Machine and Deep Learning methods on Photoplethysmography signals: the D4 report from the QUMPHY project
Urs Hackstein, Jordi Alastruey, Philip Aston +13
This report is part of the Qumphy project (22HLT01 Qumphy) that is funded by the European Union and is dedicated to the development of measures to quantify the uncertainties associ…
cs.LG2025
A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis
Ciaran Bench, Oskar Pfeffer, Vivek Desai +7
In principle, deep learning models trained on medical time-series, including wearable photoplethysmography (PPG) sensor data, can provide a means to continuously monitor physiologi…
cs.LG2025
Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches
Mohammad Moulaeifard, Loic Coquelin, Mantas Rinkevičius +13
Photoplethysmography (PPG) is a widely used non-invasive physiological sensing technique, suitable for various clinical applications. Such clinical applications are increasingly su…