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 SpO2Â 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 SpO2Â 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.
General
Imprint: |
Springer International Publishing AG
|
Country of origin: |
Switzerland |
Series: |
Springer Theses |
Release date: |
July 2023 |
First published: |
2023 |
Authors: |
Fernando Vaquerizo Villar
|
Dimensions: |
235 x 155mm (L x W) |
Pages: |
90 |
Edition: |
1st ed. 2023 |
ISBN-13: |
978-3-03-132831-2 |
Categories: |
Books
|
LSN: |
3-03-132831-0 |
Barcode: |
9783031328312 |
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