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Expanded quantitative models for assessment of respiratory diseases and monitoring

机译:扩大了呼吸系统疾病和监测的定量模型

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The industrial and demographic expansion and associated increased exposure to pollutants continue to be critical factors contributing to the development of respiratory and cardiovascular diseases. Specifically, this paper builds on previously developed quantitative models for assessment of respiratory disorders utilizing acoustical characterization, as auscultation is a primary method used in initial assessment of respiratory and cardiovascular functions. Applicable techniques used in the speech processing domain were utilized to evaluate lung sound signals obtained with a digital stethoscope. Utilization of more sensitive electronic stethoscopes and application of quantitative signal analysis methods offer opportunities for improved diagnosis in children and overall patient monitoring. Reported methodology is based on expanded Gaussian Mixed Models (GMM). These expanded models provide significantly increased levels of peculiar respiratory signal identification reaching over the 92% level, although it is accomplished at higher computational demand. This approach allows broader quantitative analysis, identification and monitoring of respiratory disorders in general.
机译:工业和人口统计扩张和相关的污染物的增加的暴露是促进呼吸系统和心血管疾病的发展的关键因素。具体而言,本文以先前开发的用于评估利用声学表征的呼吸系统疾病的定量模型构建,因为灵活性是用于初步评估呼吸系统和心血管功能的主要方法。用于语音处理结构域中使用的适用技术用于评估用数字听诊器获得的肺部声音信号。利用更敏感的电子听诊和定量信号分析方法的应用为儿童诊断和整体患者监测提供了机会。报告的方法是基于扩展的高斯混合模型(GMM)。这些扩展模型可显着提高达到92%水平的特殊呼吸系统信号鉴定水平,尽管它是在更高的计算需求下实现的。这种方法允许通常更广泛的定量分析,鉴定和监测呼吸系统障碍。

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