Authors:
Jakob Juul Larsen
;
Lars G. Johansen
and
Henrik Karstoft
Affiliation:
Aarhus University, Denmark
Keyword(s):
Electrocardiogram Derived Respiration, Evolutionary Algorithms, ECG Modeling.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Cardiovascular Signals
;
Computer Vision, Visualization and Computer Graphics
;
Evolutionary Systems
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
Abstract:
In this work we present a method to extract the respiratory signal from single lead ECG measurements, electrocardiogram derived respiration (EDR). The method is based on adaptive ECG modeling and respiratory signal estimation using an evolutionary algorithm fed with the model parameters. The evolutionary algorithm, which is allowed to employ a large constellation of functions, comes up with a set of relatively simple expressions (3-4 terms) describing valid relationships between ECG model parameters and the respiratory signal. In fact, the expressions mainly turn out to be linear combinations of the model parameters. Our preliminary experiments indicate that this method yields a robust EDR, and that this EDR correlates very well with a reference respiratory signal measurement. Correlation coefficients for the derived expressions lie around 0.95.