# 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: https://arxiv.org/abs/2604.01398 · HTML: https://mlsanity.com/paper/2604.01398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 68 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches](https://mlsanity.com/paper/2502.19949/) (2025)
- [AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning](https://mlsanity.com/paper/2302.05179/) (2023)
- [ApSense: Data-driven Algorithm in PPG-based Sleep Apnea Sensing](https://mlsanity.com/paper/2306.10863/) (2023)

## Abstract (arXiv)

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.