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Automated analysis of the oximetry s...

Vaquerizo Villar, Fernando.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automated analysis of the oximetry signal to simplify the diagnosis of pediatric sleep apnea/ by Fernando Vaquerizo Villar.
其他題名:	from feature-engineering to deep-learning approaches /
作者:	Vaquerizo Villar, Fernando.
出版者:	Cham :Springer Nature Switzerland : : 2023.,
面頁冊數:	xviii, 90 p. :ill. (chiefly color), digital ;24 cm.
附註:	"Doctoral thesis accepted by University of Valladolid, Valladolid, Spain."
內容註:	Introduction -- Hypotheses and Objectives -- Methods -- Results -- Discussion.
Contained By:	Springer Nature eBook
標題:	Sleep apnea syndromes in children - Diagnosis. -
電子資源:	https://doi.org/10.1007/978-3-031-32832-9
ISBN:	9783031328329

Automated analysis of the oximetry signal to simplify the diagnosis of pediatric sleep apnea = from feature-engineering to deep-learning approaches /
Vaquerizo Villar, Fernando.

Automated analysis of the oximetry signal to simplify the diagnosis of pediatric sleep apneafrom feature-engineering to deep-learning approaches /[electronic resource] :by Fernando Vaquerizo Villar. - Cham :Springer Nature Switzerland :2023. - xviii, 90 p. :ill. (chiefly color), digital ;24 cm. - Springer theses,2190-5061. - Springer theses..

"Doctoral thesis accepted by University of Valladolid, Valladolid, Spain."

Introduction -- Hypotheses and Objectives -- Methods -- Results -- Discussion.

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.

ISBN: 9783031328329

Standard No.: 10.1007/978-3-031-32832-9doiSubjects--Topical Terms:

3663338
Sleep apnea syndromes in children
--Diagnosis.

LC Class. No.: RJ506.S55

Dewey Class. No.: 618.928498

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