Childhood obstructive sleep apnea-hypopnea
syndrome (OSAHS) is a highly prevalent condition that
negatively affects health, performance and quality of life of
infants and young children. Early detection and treatment
improves neuropsychological and cognitive deficits linked with
the disease. The aim of this study was to assess the performance
of automated analysis of blood oxygen saturation (SpO2)
recordings as a screening tool for OSAHS. As an initial step,
statistical, spectral and nonlinear features were estimated to
compose an initial feature set. Then, fast correlation-based
filter (FCBF) was applied to search for the optimum subset.
Finally, the discrimination power (OSAHS negative vs. OSAHS
positive) of three pattern recognition algorithms was assessed:
linear discriminant analysis (LDA), quadratic discriminant
analysis (QDA) and logistic regression (LR). Three clinical cutoff
points commonly used in the literature for positive diagnosis
of the disease were applied: apnea-hypopnea index (AHI) of 1,
3 and 5 events per hour (e/h). Our methodology reached 88.6%
accuracy (71.4% sensitivity and 100.0% specificity, 100.0%
positive predictive value, and 84.0% negative predictive value)
in an independent test set using QDA for a clinical cut-off point
of 5 e/h. These results suggest that SpO2 nocturnal recordings
may be used to develop a reliable and efficient screening tool
for childhood OSAHS
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