Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

The presence of respiratory information within the electrocardiogram (ECG) signal is a well-documented phenomenon. We present a Gaussian process framework for the estimation of respiratory rate from the different sources of modulation in a single-lead ECG. We propose a periodic covariance function to model the frequency- and amplitude-modulation time series derived from the ECG, where the hyperparameters of the process are used to derive the respiratory rate. The approach is evaluated using data taken from 40 healthy subjects each with 2 hours of monitoring, containing ECG and respiration waveforms. Results indicate that the accuracy of our proposed method is comparable with that of existing methods, but with the advantages of a principled probabilistic approach, including the direct quantification of the uncertainty in the estimation.

Original publication

DOI

10.1109/embc.2013.6610147

Type

Conference paper

Publication Date

01/2013

Volume

2013

Pages

2902 - 2905

Keywords

Humans, Electrocardiography, Probability, Regression Analysis, Normal Distribution, Reproducibility of Results, Age Factors, Respiration, Supine Position, Algorithms, Models, Theoretical, Signal Processing, Computer-Assisted, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Young Adult, Respiratory Rate