Usefulness of discrete wavelet transform in the analysis of oximetry signals to assist in childhood sleep apnea-hypopnea syndrome diagnosis

Sleep apnea hypopnea syndrome (SAHS) is a highly prevalent respiratory disorder that may cause many negative consequences for the health and development of children. The gold standard for diagnosis is the overnight polysomnography (PSG), which is a high cost, complex, intrusive, and time-demanding technique. To improve the early detection of pediatric SAHS, we propose an automated analysis of the SpO₂ signal from nocturnal oximetry. A database composed of 298 SpO₂ recordings from children ranging from 0 to 13 years old was used for this purpose. Due to the abrupt changes caused by respiratory events in the SpO₂ signal, our goal was to evaluate the diagnostic ability of this by means of the discrete wavelet transform (DWT). To achieve this objective, we conducted a signal processing approach divided into two main stages: (i) feature extraction, where features from the DWT detail coefficients were computed, and (ii) feature classification, where a logistic regression (LR) model was used to classify children into SAHS negative or SAHS positive. Our results showed that respiratory events introduced more variability in two detail levels of the DWT from SpO₂: 0.024-0.049 Hz and 0.012-0.024 Hz. Moreover, the LR classifier achieved an 81.9% accuracy (79.1% sensitivity and 84.1% specificity) in an independent test set for a clinical cutoff point of 5 events/h, as derived from PSG. These results suggest that DWT analysis may be a useful tool to analyze SpO₂ recordings in the context of childhood SAHS.