RT info:eu-repo/semantics/article
T1 Feature selection from nocturnal oximetry using genetic algorithms to assist in obstructive sleep apnoea diagnosis
A1 Álvarez, Daniel
A1 Hornero, Roberto
A1 Marcos, J. Víctor
A1 del Campo, Félix
AB Nocturnal pulse oximetry (NPO) has demonstrated to be a powerful tool to help in obstructive sleep apnoea (OSA) detection. However, additional analysis is needed to use NPO alone as an alternative to nocturnal polysomnography (NPSG), which is the gold standard for a definitive diagnosis. In the present study, we exhaustively analysed a database of blood oxygen saturation (SpO2) recordings (80 OSA-negative and 160 OSA-positive) to obtain further knowledge on the usefulness of NPO. Population set was randomly divided into training and test sets. A feature extraction stage was carried out: 16 features (time and frequency statistics and spectral and nonlinear features) were computed. A genetic algorithm (GA) approach was applied in the feature selection stage. Our methodology achieved 87.5% accuracy (90.6% sensitivity and 81.3% specificity) in the test set using a logistic regression (LR) classifier with a reduced number of complementary features (3 time-domain statistics, 1 frequency-domain statistic, 1 conventional spectral feature and 1 nonlinear feature) automatically selected by means of GAs. Our results improved diagnostic performance achieved with conventional oximetric indexes commonly used by physicians. We concluded that GAs could be an effective and robust tool to search for essential oximetric features that could enhance NPO in the context of OSA diagnosis.
PB ELSEVIER
SN 1350-4533
YR 2012
FD 2012
LK https://uvadoc.uva.es/handle/10324/65593
UL https://uvadoc.uva.es/handle/10324/65593
LA eng
NO Medical Engineering & Physics, 2012, vol. 34, n. 8, p. 1049-1057.
NO Producción Científica
DS UVaDOC
RD 19-oct-2024