2026-05-05T20:54:19Zhttps://uvadoc.uva.es/oai/request
oai:uvadoc.uva.es:10324/655942025-02-20T11:59:19Zcom_10324_1191com_10324_931com_10324_894col_10324_1379
UVaDOC author Álvarez González, Daniel author Hornero Sánchez, Roberto author Marcos Martín, José Víctor author Wessel, Niels author Penzel, Thomas author Glos, Martin author Campo Matias, Félix del 2024-02-02T15:49:59Z 2024-02-02T15:49:59Z 2013 International Journal of Neural Systems, 2013, vol. 23, n. 5, p. 1-18. 0129-0657 https://uvadoc.uva.es/handle/10324/65594 10.1142/S0129065713500202 1350020-1 05 1350020-18 International Journal of Neural Systems 23 1793-6462 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. eng Assessment of feature selection and clasification approaches to enhance information from overnight oxymetry in the context of apnea diagnosis info:eu-repo/semantics/article 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URL https://uvadoc.uva.es/bitstream/10324/65594/1/%282%29Alvarez_et_al_IJNS-2013%28accepted_version%29.pdf File MD5 555a6b2607b12f392b6d0aaaddb75336 763460 application/pdf (2)Alvarez_et_al_IJNS-2013(accepted_version).pdf