Improving diagnostic ability of blood oxygen saturation from overnight pulse oximetry in obstructive sleep apnea detection by means of central tendency measure

Daniel Alvarez, Roberto Hornero, María García, Félix del Campo, Carlos Zamarrón
Artificial Intelligence in Medicine 2007, 41 (1): 13-24

OBJECTIVES: Nocturnal pulse oximetry is a widely used alternative to polysomnography (PSG) in screening for obstructive sleep apnea (OSA) syndrome. Several oximetric indexes have been derived from nocturnal blood oxygen saturation (SaO2). However, they suffer from several limitations. The present study is focused on the usefulness of nonlinear methods in deriving new measures from oximetry signals to improve the diagnostic accuracy of classical oximetric indexes. Specifically, we assessed the validity of central tendency measure (CTM) as a screening test for OSA in patients clinically suspected of suffering from this disease.

MATERIALS AND METHODS: We studied 187 subjects suspected of suffering from OSA referred to the sleep unit. A nocturnal pulse oximetry study was applied simultaneously to a conventional PSG. Three different index groups were compared. The first one was composed by classical indexes provided by our oximeter: oxygen desaturation indexes (ODIs) and cumulative time spent below a saturation of 90% (CT90). The second one was formed by indexes derived from a nonlinear method previously studied by our group: approximate entropy (ApEn). The last one was composed by indexes derived from a CTM analysis.

RESULTS: For a radius in the scatter plot equal to 1, CTM values corresponding to OSA positive patients (0.30+/-0.20, mean+/-S.D.) were significantly lower (p<0.001) than those values from OSA negative subjects (0.71+/-0.18, mean+/-S.D.). CTM was significantly correlated with classical indexes and indexes from ApEn analysis. CTM provided the highest correlation with the apnea-hipopnea index AHI (r=-0.74, p<0.0001). Moreover, it reached the best results from the receiver operating characteristics (ROC) curve analysis, with 90.1% sensitivity, 82.9% specificity, 88.5% positive predictive value, 85.1% negative predictive value, 87.2% accuracy and an area under the ROC curve of 0.924. Finally, the AHI derived from the quadratic regression curve for the CTM showed better agreement with the AHI from PSG than classical and ApEn derived indexes.

CONCLUSION: The results suggest that CTM could improve the diagnostic ability of SaO2 signals recorded from portable monitoring. CTM could be a useful tool for physicians in the diagnosis of OSA syndrome.

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