期刊全称 | Automated Analysis of the Oximetry Signal to Simplify the Diagnosis of Pediatric Sleep Apnea | 期刊简称 | From Feature-Enginee | 影响因子2023 | Fernando Vaquerizo Villar | 视频video | http://file.papertrans.cn/167/166244/166244.mp4 | 发行地址 | Nominated as an outstanding PhD thesis by the Bioengineering Group of Comité Español de Automática.Reports on novel feature engineering and deep learning approaches applied to overnight oximetry.Descr | 学科分类 | Springer Theses | 图书封面 |  | 影响因子 | .This book describes the application of novel signal processing algorithms to improve the diagnostic capability of the blood oxygen saturation signal (SpO.2.) from nocturnal oximetry in the simplification of pediatric obstructive sleep apnea (OSA) diagnosis. For this purpose, 3196 SpO.2. 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 SpO.2. 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 | Pindex | Book 2023 |
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