Paper Info
Title
Pattern recognition in airflow recordings to assist in the sleep apnoea-hypopnoea syndrome diagnosis.
Abstract
This paper aims at detecting sleep apnoea-hypopnoea syndrome (SAHS) from single-channel airflow (AF) recordings. The study involves 148 subjects. Our proposal is based on estimating the apnoea-hypopnoea index (AHI) after global analysis of AF, including the investigation of respiratory rate variability (RRV). We exhaustively characterize both AF and RRV by extracting spectral, nonlinear, and statistical features. Then, the fast correlation-based filter is used to select those relevant and non-redundant. Multiple linear regression, multi-layer perceptron (MLP), and radial basis functions are fed with the features to estimate AHI. A conventional approach, based on scoring apnoeas and hypopnoeas, is also assessed for comparison purposes. An MLP model trained with AF and RRV selected features achieved the highest agreement with the true AHI (intra-class correlation coefficient = 0.849). It also showed the highest diagnostic ability, reaching 92.5 % sensitivity, 89.5 % specificity and 91.5 % accuracy. This suggests that AF and RRV can complement each other to estimate AHI and help in SAHS diagnosis.
Year
DOI
Venue
2013
10.1007/s11517-013-1109-7
Med. Biol. Engineering and Computing
Keywords
Field
DocType
Sleep apnoea–hypopnoea syndrome, Airflow, Respiratory rate variability, AHI estimation, Pattern recognition
Correlation coefficient,Pattern recognition,Linear model,Respiratory rate,Correlation,Airflow,Artificial intelligence,Perceptron,Mathematics,Polysomnography,Linear regression
Journal
Volume
Issue
ISSN
51
12
1741-0444
Citations 
PageRank 
References 
8
0.88
14
Authors
5
Name
Order
Citations
PageRank
Gonzalo C. Gutiérrez-Tobal13110.29
Daniel Álvarez221225.22
J. Víctor Marcos3757.13
Félix del Campo412618.12
Roberto Hornero560367.74