Overview
- Nominated as an outstanding PhD thesis by the Bioengineering Group of Comité Español de Automática
- Reports on novel feature engineering and deep learning approaches applied to overnight oximetry
- Describes a novel strategy for the automated screening of pediatric sleep apnea
Part of the book series: Springer Theses (Springer Theses)
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About this book
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.
Keywords
Table of contents (6 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
Bibliographic Information
Book Title: Automated Analysis of the Oximetry Signal to Simplify the Diagnosis of Pediatric Sleep Apnea
Book Subtitle: From Feature-Engineering to Deep-Learning Approaches
Authors: Fernando Vaquerizo Villar
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-031-32832-9
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-32831-2Published: 04 July 2023
Softcover ISBN: 978-3-031-32834-3Published: 05 July 2024
eBook ISBN: 978-3-031-32832-9Published: 03 July 2023
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XVIII, 90
Number of Illustrations: 1 b/w illustrations, 17 illustrations in colour
Topics: Signal, Image and Speech Processing, Biomedical Engineering and Bioengineering, Machine Learning