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Benchmark Problems and Benchmark Datasets for the evaluation of Machine and Deep Learning methods on Photoplethysmography signals: the D4 report from the QUMPHY project

arXiv cs.LG / 4/3/2026

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Key Points

  • The QUMPHY project report (EU-funded) focuses on quantifying uncertainties for machine learning methods applied to medical signal processing, specifically photoplethysmography (PPG) signals.
  • It defines six PPG-related medical problems to be used as Benchmark Problems for evaluating machine and deep learning approaches.
  • The report also specifies suitable benchmark datasets and explains how they should be used for evaluation.
  • Overall, it provides a structured benchmark framework aimed at improving the reliability and uncertainty handling of ML models in medical PPG applications.

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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