Benchmark Problems and Benchmark Datasets for the evaluation of Machine and Deep Learning methods on Photoplethysmography signals: the D4 report from the QUMPHY project
This work provides standardized benchmarks for researchers in medical signal processing, but it is incremental as it focuses on dataset curation rather than novel algorithmic advances.
The report identifies six medical problems related to photoplethysmography (PPG) signals as benchmark problems and describes suitable benchmark datasets for evaluating machine and deep learning methods, as part of a project to quantify uncertainties in ML algorithms for medical applications.
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.