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Auteurs principaux:	Hackstein, Urs, Alastruey, Jordi, Aston, Philip, Bench, Ciaran, Charlton, Peter H., Coquelin, Loic, Hegemann, Nando, Marozas, Vaidotas, Moulaeifard, Mohammad, Nandi, Manasi, Petrenas, Andrius, Pfeffer, Oskar, Rinkevicius, Mantas, Solosenko, Andrius, Strodthoff, Nils, Vardanega, Sara
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	https://arxiv.org/abs/2604.01398
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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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