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Multichannel lung sound analysis to detect severity of lung disease in cystic fibrosis

机译：多通道肺部分析检测肺病患者囊性纤维化的严重程度

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Objective: Respiratory disease in Cystic fibrosis (CF) patients is one of the main causes of the reduction in pulmonary function and death. The primary goals of CF treatment include maintaining or improving pulmonary function and reducing the rate of pulmonary function decline. Therefore, the severity of lung disease should be monitored in CF patients. The objective of this study is to examine multichannel lung sound analysis in detecting the severity of lung disease in CF patients.Methods: 209 multichannel lung sound samples were recorded from thirty seven CF patients using a thirty channel acquisition system. Then, expiration to inspiration lung sound power ratio features in different frequency bands (E/I F) were extracted from large airway, upper airway and peripheral airway channels. These features were compared between the groups with different severity levels of the lung disease using Support Vector Machine, Artificial Neural Network, Decision tree and Naive Baysian classifiers by 'leave-one-sample-out' method.Results: It was shown that features of upper airways and peripheral airways were more effective in discriminating normal from mild (91.1%) and moderate from severe (92.8%) respiratory sound samples, respectively. The best result for discriminating between all groups of severity was related to neural network classifier which performs 89.05% average accuracy. Also, 'leave-one-subject-out' method confirmed the results.Conclusion: The proposed multichannel lung sound analysis method was successful in discriminating different severity levels of CF lung disease. Moreover, analysis of different lung region signals in consecutive levels of lung disease was consistent with regional damage of lung in CF.
机译：目的：囊性纤维化（CF）患者的呼吸系统是肺功能和死亡减少的主要原因之一。 CF处理的主要目标包括维持或改善肺功能并降低肺功能下降的速率。因此，应在CF患者中监测肺病的严重程度。本研究的目的是检查多通道肺部分析检测CF患者肺病的严重程度。方法：209次CF患者使用30次渠道采集系统记录了三十七名CF患者。然后，从大型气道，上呼吸道和外围气道通道中提取不同频带（E / I F）中的灵感肺声功率比特征的到期。通过使用支持向量机，人工神经网络，决策树和天真贝叶斯分类的肺病不同严重程度的群体之间进行这些特征，“休假 - 一个样本输出”方法。结果：显示了上呼吸道和周边气道在鉴别温和（91.1％）和中等从严重（92.8％）呼吸声样品中，更有效。所有严重性群体之间辨别的最佳结果与神经网络分类器相关，其平均精度的平均精度为89.05％。此外，'休假 - 一次性'方法证实了结果。结论：所提出的多通道肺部声音分析方法是成功的鉴别不同严重程度的CF肺病。此外，连续肺病中不同肺区信号的分析与CF中肺的区域损伤一致。

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