RT info:eu-repo/semantics/article
T1 ASSESSMENT OF FEATURE SELECTION AND CLASSIFICATION APPROACHES TO ENHANCE INFORMATION FROM OVERNIGHT OXIMETRY IN THE CONTEXT OF APNEA DIAGNOSIS
A1 ÁLVAREZ, DANIEL
A1 HORNERO, ROBERTO
A1 MARCOS, J. VÍCTOR
A1 WESSEL, NIELS
A1 PENZEL, THOMAS
A1 GLOS, MARTIN
A1 DEL CAMPO, FÉLIX
AB This study is aimed at assessing the usefulness of different feature selection and classification methodologies in the context of sleep apnea hypopnea syndrome (SAHS) detection. Feature extraction, selection and classification stages were applied to analyze blood oxygen saturation (SaO2) recordings in order to simplify polysomnography (PSG), the gold standard diagnostic methodology for SAHS. Statistical, spectral and nonlinear measures were computed to compose the initial feature set. Principal component analysis (PCA), forward stepwise feature selection (FSFS) and genetic algorithms (GAs) were applied to select feature subsets. Fisher’s linear discriminant (FLD), logistic regression (LR) and support vector machines (SVMs) were applied in the classification stage. Optimum classification algorithms from each combination of these feature selection and classification approaches were prospectively validated on datasets from two independent sleep units. FSFS+LR achieved the highest diagnostic performance using a small feature subset (4 features), reaching 83.2% accuracy in the validation set and 88.7% accuracy in the test set. Similarly, GAs+SVM also achieved high generalization capability using a small number of input features (7 features), with 84.2% accuracy on the validation set and 84.5% accuracy in the test set. Our results suggest that reduced subsets of complementary features (25% to 50% of total features) and classifiers with high generalization ability could provide high-performance screening tools in the context of SAHS.
PB WORLD SCIENTIFIC PUBLISHING
SN 0129-0657
YR 2013
FD 2013
LK https://uvadoc.uva.es/handle/10324/65594
UL https://uvadoc.uva.es/handle/10324/65594
LA eng
NO International Journal of Neural Systems, 2013, vol. 23, n. 5, p. 1-18.
NO Producción Científica
DS UVaDOC
RD 31-may-2024