This study is aimed at assessing the usefulness of variable ranking techniques for feature selection in the context of sleep apnea hypopnea syndrome (SAHS) diagnosis from blood oxygen saturation (SpO
) recordings. Time, frequency, linear, and nonlinear analyses were carried out to compose an initial feature set from oximetry. Principal component analysis (PCA) and fast correlation-based filter (FCBF) were used to derive suitable feature subsets. Support vector machines (SVMs) were applied in the classification stage. A total of 240 subjects suspected of suffering from SAHS composed the population under study. FCBF-based feature subsets significantly outperformed PCA in the test set. A SVM with 5 input features from FCBF achieved the highest performance: 86.5% sensitivity, 83.3% specificity, and 85.4% accuracy. Our results suggest that a suitable analysis of the feature space by means of variable ranking techniques could provide useful information to assist in SAHS diagnosis.