Int J Neural Syst - Assessment of feature selection and classification approaches to enhance information from overnight oximetry in the context of apnea diagnosis.

Tópicos

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Resumo

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.

Resumo Limpo

studi aim assess use differ featur select classif methodolog context sleep apnea hypopnea syndrom sah detect featur extract select classif stage appli analyz blood oxygen satur sao record order simplifi polysomnographi psg gold standard diagnost methodolog sah statist spectral nonlinear measur comput compos initi featur set princip compon analysi pca forward stepwis featur select fsfs genet algorithm gas appli select featur subset fisher linear discrimin fld logist regress lr support vector machin svms appli classif stage optimum classif algorithm combin featur select classif approach prospect valid dataset two independ sleep unit fsfs lr achiev highest diagnost perform use small featur subset featur reach accuraci valid set accuraci test set similar gas svm also achiev high general capabl use small number input featur featur accuraci valid set accuraci test set result suggest reduc subset complementari featur total featur classifi high general abil provid highperform screen tool context sah

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