An Adaptive Method for Classification of Noisy Respiratory Sounds
An Adaptive Method for Classification of Noisy Respiratory Sounds
复制标题
一种自适应呼吸噪声分类方法
DOI：
10.1109/nics54270.2021.9701460
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Khanh Nguyen
中科院分区：
文献类型：
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作者：
Khanh Nguyen
呼吸音（RSs）包含有关肺和气道阻塞的生理和病理的基本信息。因此，了解RS对呼吸道疾病的诊断具有至关重要的作用。然而，呼吸声信号中的外界噪声是本研究的一大制约因素。本文提出了一种对噪声呼吸信号进行分类的方法。首先，应用了四种自适应滤波算法（RLS、LMS、NLMS和Kalman），并对其降噪效果进行了评估。然后，利用Mel频率倒谱系数提取滤波后声音的特征。最后，利用支持向量机方法对呼吸音进行分类。我们还对来自16名健康志愿者的1980次呼吸事件数据集进行了实验。结果表明，所研究的方法中，支持向量机和卡尔曼的准确率最高，达到95.5%。
Respiratory sounds (RSs) contain essential information about the physiology and pathology of lungs and airways obstruction. Therefore, RS understanding has a critical role in diagnosing respiratory patients. However, the external noise in the respiratory sound signal is a large restriction for this study. In this paper, we propose a method to classify noisy respiratory signals. Firstly, four adaptive filtering algorithms (RLS, LMS, NLMS, and Kalman) are applied and evaluated for noise reduction. Then, we extract features of filtered sounds, using Mel Frequency Cepstral Coefficient. Finally, the SVM method is used to classify respiratory sounds. We also conducted experiments on a dataset consisting of 1980 breath events, collected from 16 healthy volunteers. The obtained results show that, the investigated methods, SVM and Kalman achieves the highest accuracy of 95.5%.