東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Automated analysis of the oximetry s...

Vaquerizo Villar, Fernando.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automated analysis of the oximetry signal to simplify the diagnosis of pediatric sleep apnea/ by Fernando Vaquerizo Villar.
Reminder of title:	from feature-engineering to deep-learning approaches /
Author:	Vaquerizo Villar, Fernando.
Published:	Cham :Springer Nature Switzerland : : 2023.,
Description:	xviii, 90 p. :ill. (chiefly color), digital ;24 cm.
Notes:	"Doctoral thesis accepted by University of Valladolid, Valladolid, Spain."
[NT 15003449]:	Introduction -- Hypotheses and Objectives -- Methods -- Results -- Discussion.
Contained By:	Springer Nature eBook
Subject:	Sleep apnea syndromes in children - Diagnosis. -
Online resource:	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 /
LDR:02840nmm a2200373 a 4500 001 2332979
003 DE-He213
005 20230703131831.0
006 m d
007 cr nn 008maaau
008 240402s2023 sz s 0 eng d
020 $a 9783031328329 $q (electronic bk.)
020 $a 9783031328312 $q (paper)
024 7 $a 10.1007/978-3-031-32832-9 $2 doi
035 $a 978-3-031-32832-9
040 $a GP $c GP
041 0 $a eng
050 4 $a RJ506.S55
072 7 $a TJF $2 bicssc
072 7 $a UYS $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TJF $2 thema
072 7 $a UYS $2 thema
082 0 4 $a 618.928498 $2 23
090 $a RJ506.S55 $b V286 2023
100 1 $a Vaquerizo Villar, Fernando. $3 3663337
245 1 0 $a Automated analysis of the oximetry signal to simplify the diagnosis of pediatric sleep apnea $h [electronic resource] : $b from feature-engineering to deep-learning approaches / $c by Fernando Vaquerizo Villar.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2023.
300 $a xviii, 90 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5061
500 $a "Doctoral thesis accepted by University of Valladolid, Valladolid, Spain."
505 0 $a Introduction -- Hypotheses and Objectives -- Methods -- Results -- Discussion.
520 $a 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.
650 0 $a Sleep apnea syndromes in children $x Diagnosis. $3 3663338
650 1 4 $a Signal, Speech and Image Processing. $3 3592727
650 2 4 $a Biomedical Devices and Instrumentation. $3 3595636
650 2 4 $a Machine Learning. $3 3382522
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer theses. $3 1314442
856 4 0 $u https://doi.org/10.1007/978-3-031-32832-9
950 $a Engineering (SpringerNature-11647)