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

TL;DR

This paper presents benchmark problems and datasets for evaluating machine learning methods on PPG signals, aiding in uncertainty quantification in medical applications.

Contribution

It introduces six benchmark problems and describes suitable datasets for assessing machine learning techniques on PPG signals.

Findings

Six benchmark problems related to PPG signals are identified.

Suitable datasets for each benchmark problem are described.

The report supports uncertainty quantification in medical machine learning.

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.