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082 0 4 _a621.382
_223
100 1 _aVaquerizo Villar, Fernando.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aAutomated Analysis of the Oximetry Signal to Simplify the Diagnosis of Pediatric Sleep Apnea
_h[electronic resource] :
_bFrom Feature-Engineering to Deep-Learning Approaches /
_cby Fernando Vaquerizo Villar.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2023.
300 _aXVIII, 90 p. 18 illus., 17 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5061
500 _aAcceso multiusuario
505 0 _aIntroduction -- Hypotheses and Objectives -- Methods -- Results -- Discussion.
520 _aThis 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.
541 _fUABC ;
_cPerpetuidad
650 0 _aSignal processing.
650 0 _aBiomedical engineering.
650 0 _aMachine learning.
650 1 4 _aSignal, Speech and Image Processing .
650 2 4 _aBiomedical Devices and Instrumentation.
650 2 4 _aMachine Learning.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031328312
776 0 8 _iPrinted edition:
_z9783031328336
776 0 8 _iPrinted edition:
_z9783031328343
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5061
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-32832-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
999 _c262048
_d262047