Synthetic realistic noise-corrupted PPG database and noise generator for the evaluation of PPG denoising and delineation algorithms
This database is meant to evaluate the performance of denoising and delineation algorithms for PPG signals affected by noise. The noise generator allows applying the algorithms under test to an artificially corrupted reference PPG signal and comparing its output to the output obtained with the original signal. Moreover, the noise generator can produce artifacts of variable intensities, permitting the evaluation of the algorithms' performance against different noise levels. The reference signal is a PPG sample of a healthy subject at rest during a relaxing session.
2021
Funder | Grant Number |
US foundations | ONR-G N62909-20-1-2063 |
FNS | NSF 200020182009 |
Other foundations | MyPreHealth 16073 |
Relation | URL/DOI |
IsDerivedFrom | |
IsSupplementedBy | |