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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.01398 |
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| _version_ | 1866911562127638528 |
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01398 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Benchmark Problems and Benchmark Datasets for the evaluation of Machine and Deep Learning methods on Photoplethysmography signals: the D4 report from the QUMPHY project 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 Machine Learning 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. |
| title | Benchmark Problems and Benchmark Datasets for the evaluation of Machine and Deep Learning methods on Photoplethysmography signals: the D4 report from the QUMPHY project |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.01398 |