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Oxygen saturation regularity analysis in the diagnosis of obstructive sleep apnea

del Campo, F, Hornero, R, Zamarrón, C, Abásolo, D and Álvarez, D (2006) Oxygen saturation regularity analysis in the diagnosis of obstructive sleep apnea Artificial Intelligence in Medicine, 37 (2). 111 - 118. ISSN 0933-3657

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Objective The present study assessed the validity of approximate entropy (ApEn) analysis of arterial oxygen saturation (SaO2) data obtained from pulse oximetric recordings as a diagnostic test for obstructive sleep apnea (OSA) in patients clinically suspected of suffering this disease. Methodology A sample of 187 referred outpatients, clinically suspected of having OSA, was studied using nocturnal pulse oximetric recording performed simultaneously with complete polysomnography. ApEn analysis was applied to SaO2 data. Results Patients with OSA presented significantly higher approximate entropy levels than those without OSA (1.08±0.30 versus 0.47±0.26). Apnea–hypopnea index was correlated significantly with ApEn (r=0.607; p<0.001). Using receiver operating characteristic curve analysis, we obtained a diagnostic sensitivity of 88.3% and specificity of 82.9%, positive predictive value of 88.3% and a negative predictive value of 82.9%, at a threshold of 0.679. As a diagnostic test, this method presents high sensitivity and specificity compared to traditional methods in the diagnosis of OSA. Conclusion We conclude that ApEn analysis of SaO2 data obtained from pulse oximetric recordings could be useful as a diagnostic technique for OSA subjects.

Item Type: Article
Additional Information: NOTICE: this is the author’s version of a work that was accepted for publication in Artificial Intelligence in Medicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Artificial Intelligence in Medicine, 37(2), June 2006,
Divisions: Faculty of Engineering and Physical Sciences > Mechanical Engineering Sciences
Depositing User: Symplectic Elements
Date Deposited: 29 Oct 2012 21:07
Last Modified: 23 Sep 2013 19:36

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