Sleep Osa

Obstructive sleep apnea (OSA) is a common disease that may affect up to 50% of the adult population and whose incidence, as well as its health and socio-economic burden, continues to rise. OSA is a well-known risk factor for motor vehicle accidents and decreased work performance, and it is frequently accompanied by cardiovascular diseases.

This book examines the correlation of the coronavirus disease-19 [CE1] [AAS2] (COVID-19) infection with sleep, circadian rhythm, and sleep disorders. The chapters of the book explore sleep problems during the COVID-19 pandemic and its association with psychological distress and also review the prevalence of clinical cases of insomnia, anxiety, and depression in COVID-19 patients. The book further presents the correlation between sleep, circadian rhythms, and immunity that contributes toward more severe COVID-19. It summarizes the evidence on the interplay between circadian biology, sleep, and COVID-19. The chapters of the book discuss the relationship between obstructive sleep apnea (OSA) and adverse COVID-19 outcomes. Towards the end, the book presents studies on the anti-inflammatory, antioxidant, and immunotropic properties of melatonin and provides mechanistic insights into the potential therapeutic use of melatonin during the SARS-CoV-2 infection. The last chapter describes the publication output of sleep-related research during the pandemic and provides an overview and trends on sleep and covid-19 publication output. This book is an excellent source for neurologists, sleep specialists, and public health specialists.

This book describes the application of novel signal processing algorithms to improve the diagnostic capability of the blood oxygen saturation signal (SpO2) from nocturnal oximetry in the simplification of pediatric obstructive sleep apnea (OSA) diagnosis. For this purpose, 3196 SpO2 recordings from three different databases were analyzed using feature-engineering and deep-learning methodologies. Particularly, three novel feature extraction algorithms (bispectrum, wavelet, and detrended fluctuation analysis), as well as a novel deep-learning architecture based on convolutional neural networks are proposed. The proposed feature-engineering and deep-learning models outperformed conventional features from the oximetry signal, as well as state-of-the-art approaches. On the one hand, this book shows that bispectrum, wavelet, and detrended fluctuation analysis can be used to characterize changes in the SpO2 signal caused by apneic events in pediatric subjects. On the other hand, it demonstrates that deep-learning algorithms can learn complex features from oximetry dynamics that allow to enhance the diagnostic capability of nocturnal oximetry in the context of childhood OSA. All in all, this book offers a comprehensive and timely guide to the use of signal processing and AI methods in the diagnosis of pediatric OSA, including novel methodological insights concerning the automated analysis of the oximetry signal. It also discusses some open questions for future research.