Background. Standard pediatric in-lab polysomnography (PSG) is
relatively unavailable and particularly intrusive for children. In low resource
settings, nocturnal oximetry has been proposed as a feasible and
potentially reliable screening tool for childhood obstructive sleep apneahypopnea
syndrome (OSAHS), although additional confirmatory evidence is
needed.
Aims and objectives. Discrete wavelet transform (DWT) could be a useful
tool to characterize fluctuations in nocturnal oximetry. We aimed at
designing and assessing a model for detecting childhood OSAHS using
anthropometric and DWT features.
Methods. A total of 298 children with clinical suspicion of OSAHS
underwent in-lab PSG. A cut-off of 5 events/h was stipulated as confirming
OSAHS. DWT was used to inspect the spectral content of oximetry in
frequency bands linked with apnea pseudo-periodicity: detail levels D9
(0.024-0.049 Hz) and D10 (0.012-0.024 Hz). Mean, variance, minimum, and maximum of DWT coefficients were computed. Stepwise logistic regression
was employed to build an OSAHS model from DWT, age, gender, and body
mass index (BMI) z score. Training (60%) and test (40%) sets were
randomly allocated.
Results. Age, gender, D9 mean, and D10 variance were automatically
selected. Our model reached 79.1% sensitivity, 81.7% specificity, 4.33 LR+,
0.26 LR-, and 80.5% accuracy in the test set.
Conclusions. Features from DWT coefficients and anthropometric
variables such as age provide complementary information that enables
detection of moderate-to-severe childhood OSAHS in a high pre-test
probability cohort.
