用咳嗽声诊断和筛查肺部疾病

Christian Infante, Daniel B. Chamberlain, Rich Fletcher, Yogesh Thorat, R. Kodgule
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引用次数: 35

摘要

咳嗽声分析作为一种潜在的低成本诊断工具引起了人们的兴趣,在资源匮乏的环境中,肺部疾病的负担相当高。然而,已发表的咳嗽声分析结果通常仅限于特定肺部疾病(例如百日咳的检测),而且研究规模很小。在本文中,我们提出了一个咳嗽声分析的总体框架,其中包括自动咳嗽分割,特征提取和一般分类设计,可以应用于广泛的肺部疾病。在我们的分析中,选择了三个基于证据的特征(方差、峰度和零交叉不规则性)以及我们开发的附加特征(衰减率)。我们的咳嗽声音分析框架使用从54名患有肺部疾病(COPD,哮喘和过敏性鼻炎)的患者中收集的自愿咳嗽数据进行测试,这些患者从所有到达肺部诊所的患者以及33名健康个体中均匀取样。所有研究对象均接受听诊器听诊、临床问卷和峰值血流仪检查,并进行全肺功能检查(肺活量计、体容积描记仪、DLCO),这是确定每位患者诊断的金标准。当分类器仅使用咳嗽声进行训练时,健康与不健康的准确率(由ROC曲线的AUC确定)为74%,阻塞性与非阻塞性为80%,哮喘与COPD为81%。我们还将我们的咳嗽声分析与其他低成本诊断工具的性能进行了比较,并观察到咳嗽声的性能出乎意料地优于单独的肺音听诊,但与我们的临床问卷或峰值流量计测试相比,其性能明显较低。从这些数据中,我们得出结论,咳嗽声作为一种快速而简单的筛查工具具有价值,但与临床问卷或峰值流量计相比,诊断价值较低。
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Use of cough sounds for diagnosis and screening of pulmonary disease
Cough sound analysis has attracted interest as a potential low-cost diagnostic tool for low-resource settings, where the burden of pulmonary disease is quite high. However, published results on cough sound analysis are generally limited to specific pulmonary diseases (e.g. detection of Whooping cough — Pertussis) and the study sizes are small. In this paper, we present a general framework for cough sound analysis, which includes automatic cough segmentation, feature extraction and a general classification design that can be applied to a wide range of pulmonary diseases. For our analysis, three evidence-based features were selected (variance, kurtosis, and zero crossing irregularity) as well as an additional feature that we developed (rate of decay). Our cough sound analysis framework was tested using voluntary cough data collected from 54 patients presenting a combination of pulmonary conditions (COPD, asthma, and allergic rhinitis) equally sampled from all patients arriving at a pulmonary clinic, as well as 33 healthy individuals. All study subjects were examined with a stethoscope auscultation, clinical questionnaire, and peak flow meter, and were given a full pulmonary function test (spirometer, body plethysmograph, DLCO), which was the gold standard used to determine each patient's diagnosis. When the classifiers were trained using cough sounds alone, the accuracy (as determined by the AUC of the ROC curve) was 74% for Healthy vs Unhealthy, 80% for Obstructive vs non-Obstructive, and 81% for Asthma vs COPD. We also compared the performance of our cough sound analysis against other low-cost diagnostic tools and observed that cough sounds surprisingly had better performance than lung sound auscultation alone, but had significantly lower performance compared to our clinical questionnaire or peak flow meter test. From these data, we conclude that cough sounds have value as a rapid and simple screening tool, but are of less diagnostic value compared to a clinical questionnaire or peak flow meter.
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