Automated analysis of the oximetry signal to simplify the diagnosis of pediatric sleep apnea: from feature-engineering to deep-learning approaches

Abstract

Obstructive sleep apnea (OSA) is a high prevalent respiratory disorder in the pediatric population (1%-5%). Untreated pediatric OSA is associated with significant adverse consequences affecting metabolic, cardiovascular, neurocognitive, and behavioral systems, thus resulting in a decline of overall health and quality of life. Consequently, it is of paramount importance to accelerate the diagnosis and treatment in these children. Overnight polysomnography (PSG) is the gold standard to diagnose OSA in children. This test requires an overnight stay of pediatric subjects in a specialized sleep laboratory, as well as the recording of up to 32 biomedical signals. These recordings are used to quantify respiratory events in order to obtain the apnea-hyponea index (AHI), which is used to establish pediatric OSA severity. Nonetheless, PSG is technically complex, time-consuming, costly, highly intrusive for the children, and relatively unavailable, thus delaying the access for both the diagnosis and treatment. Consequently, simplified diagnostic techniques become necessary. In an effort to overcome these drawbacks and increase the accessibility of pediatric OSA diagnosis, many simplified alternative procedures have been developed. Among these, a common approach is the analysis of the blood oxygen saturation (SpO2) signal from overnight oximetry due to its easy acquisition and interpretation, as well as its suitability for children. Many studies have demonstrated the utility of the automated analysis of SpO2 recordings to help in adult OSA diagnosis. Conversely, the preceding studies focused on pediatric patients reported lower accuracies than those reached in the case of adults, suggesting the need to seek novel signal processing algorithms that provide additional information from the SpO2 signal for the particularities of childhood OSA. In the present Doctoral Thesis, we hypothesize that the application of novel feature extraction and deep-learning algorithms could increase the diagnostic ability of the oximetry signal in the context of pediatric OSA. Consequently, the general objective of this Doctoral Thesis is to design, develop, and assess novel clinical decision-support models in the context of childhood OSA based on the automated analysis of the SpO2 signal.