Paper Info
Title
Asthmatic Subjects Stratification Using Autonomic Nervous System Information
Abstract
Objective: the aim of this study is to evaluate whether noninvasive autonomic activity assessment could represent a potential tool for the stratification of asthmatic subjects based on symptoms control, using only 10-min electrocardiographic and respiratory signals. Methods: several heart rate variability (HRV) derived indexes, which are regarded as surrogates of autonomic activity, were evaluated in a group of asthmatic patients classified based on their symptomatology control. The effect of respiration on HRV was mitigated by means of orthogonal subspace projection. The most relevant features were used for training different classifiers. Results: similar classification performance was obtained when using HRV or clinical features, with just a 10% decrease in accuracy when using the HRV features (80% vs. 70%). This classification performance is equivalent to that achieved in new patients using the current asthma control tests. Conclusion: results suggest that the noninvasive assessment of autonomic activity could represent an added value for the monitoring of asthmatic subjects outside the clinic, using less cumbersome equipment, and therefore being suitable for an objective asthma self-monitoring. Significance:: This study provides evidence on the usefulness of noninvasive autonomic activity assessment for asthma control stratification, supporting it as a potential complement to the current clinical practice.
Year
DOI
Venue
2021
10.1016/j.bspc.2021.102802
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Keywords
DocType
Volume
Asthma, Autonomic nervous system, Heart rate variability, Asthma control, Machine learning
Journal
69
ISSN
Citations 
PageRank 
1746-8094
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Javier Milagro103.38
Lorena Soto-Retes200.34
Jordi Giner300.34
Carolina Varon49222.90
P Laguna525574.15
Raquel Bailón617631.28
Vicente Plaza700.34
Eduardo Gil86119.54