LEADER 03744nam 22005775 450 001 9910734849403321 005 20230703131831.0 010 $a3-031-32832-9 024 7 $a10.1007/978-3-031-32832-9 035 $a(MiAaPQ)EBC30616890 035 $a(Au-PeEL)EBL30616890 035 $a(DE-He213)978-3-031-32832-9 035 $a(PPN)272251917 035 $a(CKB)27442518400041 035 $a(EXLCZ)9927442518400041 100 $a20230703d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAutomated Analysis of the Oximetry Signal to Simplify the Diagnosis of Pediatric Sleep Apnea $eFrom Feature-Engineering to Deep-Learning Approaches /$fby Fernando Vaquerizo Villar 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (104 pages) 225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5061 311 08$aPrint version: Vaquerizo Villar, Fernando Automated Analysis of the Oximetry Signal to Simplify the Diagnosis of Pediatric Sleep Apnea Cham : Springer International Publishing AG,c2023 9783031328312 327 $aIntroduction -- Hypotheses and Objectives -- Methods -- Results -- Discussion. 330 $aThis book describes the application of novel signal processing algorithms to improve the diagnostic capability of the blood oxygen saturation signal (SpO2) from nocturnal oximetry in the simplification of pediatric obstructive sleep apnea (OSA) diagnosis. For this purpose, 3196 SpO2 recordings from three different databases were analyzed using feature-engineering and deep-learning methodologies. Particularly, three novel feature extraction algorithms (bispectrum, wavelet, and detrended fluctuation analysis), as well as a novel deep-learning architecture based on convolutional neural networks are proposed. The proposed feature-engineering and deep-learning models outperformed conventional features from the oximetry signal, as well as state-of-the-art approaches. On the one hand, this book shows that bispectrum, wavelet, and detrended fluctuation analysis can be used to characterize changes in the SpO2 signal caused by apneic events in pediatric subjects. On the other hand, it demonstrates that deep-learning algorithms can learn complex features from oximetry dynamics that allow to enhance the diagnostic capability of nocturnal oximetry in the context of childhood OSA. All in all, this book offers a comprehensive and timely guide to the use of signal processing and AI methods in the diagnosis of pediatric OSA, including novel methodological insights concerning the automated analysis of the oximetry signal. It also discusses some open questions for future research. 410 0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5061 606 $aSignal processing 606 $aBiomedical engineering 606 $aMachine learning 606 $aSignal, Speech and Image Processing 606 $aBiomedical Devices and Instrumentation 606 $aMachine Learning 615 0$aSignal processing. 615 0$aBiomedical engineering. 615 0$aMachine learning. 615 14$aSignal, Speech and Image Processing . 615 24$aBiomedical Devices and Instrumentation. 615 24$aMachine Learning. 676 $a621.382 700 $aVaquerizo Villar$b Fernando$01372978 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910734849403321 996 $aAutomated Analysis of the Oximetry Signal to Simplify the Diagnosis of Pediatric Sleep Apnea$93403886 997 $aUNINA