Automated Analysis of the Oximetry Signal to Simplify the Diagnosis of Pediatric Sleep Apnea

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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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 a...