Pediatric obstructive sleep apnea (OSA) is a highly prevalent and underdiagnosed breathing disease that can adversely affect the physiological and cognitive functions of children, causing serious long-term neurocognitive deficiencies, behavioral disorders, cardiometabolic malfunctioning, and endocrine dysfunctions. Consequently, it is of the utmost importance that they are timely diagnosed and treated to prevent these negative consequences.
In this context, the Doctoral Thesis focuses on designing, implementing, and assessing novel automatic signal processing methods that allow comprehensively characterizing the overnight airflow (AF) from children and helping in the pediatric OSA diagnosis. Particularly, non-linear, spectral, bispectral, recurrence plot, and wavelet analyses are proposed for adapting to the intrinsic properties of overnight AF and getting useful OSA-related information from it. In view of the results, we can conclude that the characterization of overnight AF by means of these novel methods can help to simplify the OSA diagnosis in children. In addition, the high performance of the proposed models suggests that they could be incorporated into clinical practice as reliable automatic screening methods for pediatric OSA.
