2014 | OriginalPaper | Chapter
Applying Variable Ranking to Oximetric Recordings in Sleep Apnea Diagnosis
Authors : D. Álvarez, G. C. Gutiérrez-Tobal, J. Gómez-Pilar, F. del Campo, M. López, R. Hornero
Published in: XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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
2
) 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.