Predicting spirometry readings using cough sound features and regression
Predicting spirometry readings using cough sound features and regression
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DOI：
10.1088/1361-6579/aad948
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发表时间：
2018-09-01
影响因子：
3.2
通讯作者：
Porter, Paul
中科院分区：
文献类型：
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作者：
Sharan, Roneel V.;Abeyratne, Udantha R.;Porter, Paul
目的：肺功能测定是一种常用的肺功能检测方法。它可用于哮喘和慢性阻塞性肺疾病（COPD）等疾病的明确诊断。然而，肺功能测定需要合作的患者，经验丰富的工作人员，并重复测试，以确保测量的一致性。有不适与肺量测定和一些患者无法完成测试。在本文中，我们调查的可能性，使用咳嗽声分析预测肺功能测定。方法：我们的方法是基于这样一个前提，即咳嗽产生的机制和肺量测定法的用力呼气动作具有足够的相似性，从而能够进行这种预测。使用iPhone，我们收集了322名成年人的主要是自愿咳嗽声，他们前往呼吸功能实验室进行肺功能测试。受试者有以下诊断：阻塞性、限制性或混合型疾病，或发现没有肺部疾病沿着正常肺功能测定。使用沙兰等人（2018 IEEE Trans. Biomed. Eng.）。然后，我们用各种咳嗽声音描述符表示咳嗽声音，并建立线性和非线性回归模型，将其与肺功能测定参数联系起来。还试验了用受试者人口统计学数据增强咳嗽特征。将数据集分为272个训练主题和50个测试主题进行实验。主要结果：对从总体数据集中随机选择的49名受试者评价了自动分割算法的性能，灵敏度和PPV分别为84.95%和98.51%。我们的回归模型在测试数据集上实现了标准肺量测定参数FEV 1、FVC和FEV 1/FVC的均方根误差（和相关系数）分别为0.593 L（0.810）、0.725 L（0.749）和0.164（0.547）。此外，我们可以达到70%或更高的灵敏度，特异性和准确性，通过应用GOLD标准对COPD诊断的肺功能检测结果估计。重要性：实验结果表明，预测FEV 1和FVC高度正相关，预测FEV 1/FVC中度正相关。结果表明，使用咳嗽音分析预测肺功能测定结果的可能性。
Objective: Spirometry is a commonly used method of measuring lung function. It is useful in the definitive diagnosis of diseases such as asthma and chronic obstructive pulmonary disease (COPD). However, spirometry requires cooperative patients, experienced staff, and repeated testing to ensure the consistency of measurements. There is discomfort associated with spirometry and some patients are not able to complete the test. In this paper, we investigate the possibility of using cough sound analysis for the prediction of spirometry measurements. Approach: Our approach is based on the premise that the mechanism of cough generation and the forced expiratory maneuver of spirometry share sufficient similarities enabling this prediction. Using an iPhone, we collected mostly voluntary cough sounds from 322 adults presenting to a respiratory function laboratory for pulmonary function testing. Subjects had the following diagnoses: obstructive, restrictive, or mixed pattern diseases, or were found to have no lung disease along with normal spirometry. The cough sounds were automatically segmented using the algorithm described in Sharan et al (2018 IEEE Trans. Biomed. Eng.). We then represented cough sounds with various cough sound descriptors and built linear and nonlinear regression models connecting them to spirometry parameters. Augmentation of cough features with subject demographic data is also experimented with. The dataset was divided into 272 training subjects and 50 test subjects for experimentation. Main results: The performance of the auto-segmentation algorithm was evaluated on 49 randomly selected subjects from the overall dataset with a sensitivity and PPV of 84.95% and 98.51%, respectively. Our regression models achieved a root mean square error (and correlation coefficient) for standard spirometry parameters FEV1, FVC, and FEV1/FVC of 0.593L (0.810), 0.725L (0.749), and 0.164 (0.547), respectively, on the test dataset. In addition, we could achieve sensitivity, specificity, and accuracy of 70% or higher by applying the GOLD standard for COPD diagnosis on the estimated spirometry test results. Significance: The experimental results show high positive correlation in predicting FEV1 and FVC and moderate positive correlation in predicting FEV1/FVC. The results show possibility of predicting spirometry results using cough sound analysis.