Determining respiratory rate from photoplethysmogram and electrocardiogram signals using respiratory quality indices and neural networks.

ABSTRACT: Continuous and non-invasive respiratory rate (RR) monitoring would significantly improve patient outcomes. Currently, RR is under-recorded in clinical environments and is often measured by manually counting breaths. In this work, we investigate the use of respiratory signal quality quantification and several neural network (NN) structures for improved RR estimation. We extract respiratory modulation signals from the electrocardiogram (ECG) and photoplethysmogram (PPG) signals, and calculate a possible RR from each extracted signal. We develop a straightforward and efficient respiratory quality index (RQI) scheme that determines the quality of each moonddulation-extracted respiration signal. We then develop NNs for the estimation of RR, using estimated RRs and their corresponding quality in

SUBMITTER: Baker S 

PROVIDER: S-EPMC8031461 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature