EU-funded QUMPHY project establishes benchmark standards for evaluating machine learning reliability in photoplethysmography medical signal analysis

The QUMPHY project (22HLT01), funded by the European Union, focuses on measuring uncertainties in machine learning algorithms applied to healthcare

Report identifies six medical benchmark problems related to photoplethysmography (PPG) signals for standardized ML/deep learning evaluation

Provides curated benchmark datasets and usage guidelines to enable consistent comparison of different machine learning methods on PPG signal processing tasks