为心率等生理信号分析提供6个标准评测问题与数据集。

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

• 提出6个与光电容积脉搏波信号相关的医学评测任务。
• 构建配套基准数据集并明确使用方法，支持算法公平评估。
• 适合医疗AI研究者用于模型验证与比较，尤其关注不确定性量化。

本报告是欧盟资助的QUMPHY项目（22HLT01 Qumphy）的一部分，致力于建立机器学习算法在医疗应用中，特别是光电容积脉搏波（PPG）信号分析与处理时的不确定性量化方法。报告列出了6个与PPG信号相关的医学问题作为基准评测任务，并详细描述了相应的基准数据集及其使用方式，旨在为机器学习与深度学习方法在生理信号处理中的评估提供标准化参考。

原文摘要 · Abstract (English)

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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