Analysis and classification of oximetry recordings to predict obstructive sleep apnea severity in children

Current study is focused around the potential use of oximetry to determine the obstructive sleep apnea-hypopnea syndrome (OSAHS) severity in children. Single-channel SpO₂ recordings from 176 children were divided into three severity groups according to the apnea-hypopnea index (AHI): AHI<;1 events per hour (e/h), 1≤AHI<;5 e/h, and AHI ≥5 e/h. Spectral analysis was conducted to define and characterize a frequency band of interest in SpO₂. Then we combined the spectral data with the 3% oxygen desaturation index (ODI₃) by means of a multi-layer perceptron (MLP) neural network, in order to classify children into one of the three OSAHS severity groups. Following our MLP multiclass approach, a diagnostic protocol with capability to reduce the need of polysomnography tests by 46% could be derived. Moreover, our proposal can be also evaluated, in a binary classification task for two common AHI diagnostic cutoffs (AHI = 1 e/h and AHI= 5 e/h). High diagnostic ability was reached in both cases (84.7% and 85.8% accuracy, respectively) outperforming the clinical variable ODI₃ as well as other measures reported in recent studies. These results suggest that the information contained in SpO₂ could be helpful in pediatric OSAHS severity detection.