title: Feature selection from nocturnal oximetry using genetic algorithms to assist in obstructive sleep apnoea diagnosis.
abstract: 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 (SpO(2)) 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.
authors.affiliation: Biomedial Engineering Group, E.T.S.I. de Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain. dalvgon@gmail.com
authors.forename: Daniel
authors.initials: D
authors.lastname: Álvarez
authors.forename: Roberto
authors.initials: R
authors.lastname: Hornero
authors.forename: J Víctor
authors.initials: JV
authors.lastname: Marcos
authors.forename: Félix
authors.initials: F
authors.lastname: Del Campo
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D010092:Oximetry / Q000379:methods*; D010100:Oxygen / Q000097:blood; D012815:Signal Processing, Computer-Assisted; D020181:Sleep Apnea, Obstructive / Q000097:blood / Q000175:diagnosis*; D013997:Time Factors
chemical_list: D010100:Oxygen