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Quantitative models for assessment of respiratory diseases

机译:评估呼吸系统疾病的定量模型

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Experienced dynamic demographic growth of many cities, especially in the last three decades, has often resulted in a replacement of areas with vegetation and crops by industrial parks and residential areas, exposing areas to erosion. Mexicali, which is located in an arid region, is experiencing such transformations which are paralleled by a significant increase in respiratory diseases and allergies especially in children. In particular, adverse health effects of PM10 particles and asthma can be observed. The focus of this paper is to present a method for a quantitative assessment of patient health as it relates to respiratory disorders utilizing lung sounds. In order to accomplish this, applicable traditional techniques within the speech processing domain were utilized to evaluate lung sounds obtained with a digital stethoscope. Traditional methods utilized in the evaluation of asthma involve auscultation and spirometry, but utilization of more sensitive electronic stethoscopes, which are currently available, and application of quantitative signal analysis methods offer opportunities of improved diagnosis. In particular we propose acoustic evaluation methodology based on the Gaussian Mixed Models (GMM) which should assist in broader analysis, identification, and diagnosis of asthma based on the frequency domain analysis of wheezing and crackles.
机译:经历了许多城市的动态人口增长,特别是在过去的三十年中,经常导致工业园区和住宅区的植被和农作物的地区更换,将区域暴露在侵蚀。位于干旱地区的Mexicali正在经历这种转化,这些转化并平行于呼吸系统疾病和过敏的显着增加,特别是儿童。特别地,可以观察到PM 10-IM>颗粒和哮喘的不利健康影响。本文的重点是提出一种用于定量评估患者健康的方法,因为它涉及利用肺部声音的呼吸系统疾病。为了实现这一点,利用语音处理结构域内的适用传统技术来评估用数字听诊器获得的肺部声音。在哮喘评估中使用的传统方法涉及听诊和肺活量测定法,但利用当前可用的更敏感的电子听诊器,以及定量信号分析方法的应用提供了改善诊断的机会。特别是我们提出了基于高斯混合模型(GMM)的声学评估方法,该方法应该根据喘息和噼啪声的频域分析有助于更广泛的分析,鉴定和诊断哮喘。

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