基于分位向量的正常肺音验证

P. Mayorga, C. Druzgalski, O. H. González
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引用次数: 5

摘要

与人口增长有关的人为活动影响整体健康,并导致心血管和呼吸系统疾病发病率上升。在本文中,我们提出了肺声自动验证(LSAV)和其他模式来表示使用数字听诊器听诊获得的肺声信号。分位数的使用允许a)以较小的计算需求进行更容易和客观的评估,b)建立比以前报道的更简单的高斯混合模型(GMM),以及c)在健康LS验证中几乎可以忽略不计的误差。这些方法将肺声能量与其特征频率分量联系起来,除了提供可靠的验证技术外,还简化了正常肺声识别。一旦对LS进行评估,如果单个听诊评估属于正常或异常指标的范畴,则可以简化分类,从而为更广泛的医学评估提供工具,而不依赖于通常情况下对这些声音的定性和主观描述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantile vectors based verification of normal lung sounds
Anthropogenic activities associated to population growth impact overall health and contribute to elevated rates of cardiovascular and respiratory diseases. In this paper we propose the Lung Sound Automatic Verification (LSAV), and other modalities to represent acoustic lung signals obtained by auscultation using a digital stethoscope. The utilization of quantiles allowed a) an easier and objective assessment with smaller computational demand, b) building of less-complex Gaussian Mixed Models (GMM) than those reported previously, and c) to reach practically negligible error in healthy LS verification. These approaches relate the lung sound energy to its characteristic frequency components, which in addition to a reliable verification technique simplified the normal lung sound recognition. Once the LS are evaluated, it would be possible to simplify classification if an individual auscultatory evaluation falls into the category of normal or abnormal indicators thus providing a tool for broader medical evaluation which does not rely, as it is often the case, on a qualitative and subjective description of these sounds.
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