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      Analysis of V-Net Architecture for Iris Segmentation in Unconstrained Scenarios

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      SN Computer Science
      Springer Science and Business Media LLC

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          Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use

          Pulse oximetry is routinely used to non-invasively monitor oxygen saturation levels. A low oxygen level in the blood means low oxygen in the tissues, which can ultimately lead to organ failure. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous oxygen saturation time series variability analysis. The primary objective of this research was to identify, implement and validate key digital oximetry biomarkers (OBMs) for the purpose of creating a standard and associated reference toolbox for continuous oximetry time series analysis. We review the sleep medicine literature to identify clinically relevant OBMs. We implement these biomarkers and demonstrate their clinical value within the context of obstructive sleep apnea (OSA) diagnosis on a total of n = 3806 individual polysomnography recordings totaling 26,686 h of continuous data. A total of 44 digital oximetry biomarkers were implemented. Reference ranges for each biomarker are provided for individuals with mild, moderate, and severe OSA and for non-OSA recordings. Linear regression analysis between biomarkers and the apnea hypopnea index (AHI) showed a high correlation, which reached \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline R ^2 = 0.82$$\end{document} R ¯ 2 = 0.82 . The resulting python OBM toolbox, denoted “pobm”, was contributed to the open software PhysioZoo (physiozoo.org). Studying the variability of the continuous oxygen saturation time series using pbom may provide information on the underlying physiological control systems and enhance our understanding of the manifestations and etiology of diseases, with emphasis on respiratory diseases.
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            UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation

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              ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis

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                Author and article information

                Journal
                SN Computer Science
                SN COMPUT. SCI.
                Springer Science and Business Media LLC
                2662-995X
                2661-8907
                May 2022
                April 03 2022
                May 2022
                : 3
                : 3
                Article
                10.1007/s42979-022-01113-0
                5dc60b4d-e433-46e3-a93a-c4d2ab96daa6
                © 2022

                https://www.springer.com/tdm

                https://www.springer.com/tdm

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