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arXiv · 2604.01398

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·Ciaran Bench·Peter H. Charlton·Loic Coquelin·Nando Hegemann·Vaidotas Marozas·Mohammad Moulaeifard·Manasi Nandi·Andrius Petrenas·Oskar Pfeffer·Mantas Rinkevicius·Andrius Solosenko·Nils Strodthoff·Sara Vardanega
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Abstract

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 associated with Machine Learning algorithms applied to medical problems, in particular the analysis and processing of Photoplethysmography (PPG) signals. In this report, a list of six medical problems that are related to PPG signals and serve as Benchmark Problems is given. Suitable Benchmark datasets and their usage are described also.

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Urs Hackstein, Jordi Alastruey, Philip Aston, Ciaran Bench, Peter H. Charlton, Loic Coquelin, Nando Hegemann, Vaidotas Marozas, Mohammad Moulaeifard, Manasi Nandi, Andrius Petrenas, Oskar Pfeffer, Mantas Rinkevicius, Andrius Solosenko, Nils Strodthoff, Sara Vardanega. 2026-04-01. Benchmark Problems and Benchmark Datasets for the evaluation of Machine and Deep Learning methods on Photoplethysmography signals: the D4 report from the QUMPHY project. https://arxiv.org/abs/2604.01398

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