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

2013

Volume

2013

Pages

2902 - 2905

Keywords

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