Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data
Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data
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DOI：
10.3390/s20041214
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发表时间：
2020-02-01
期刊：
影响因子：
3.9
通讯作者：
Alaiz-Moreton, Hector
中科院分区：
文献类型：
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作者：
Teresa Garcia-Ordas, Maria;Alberto Benitez-Andrades, Jose;Alaiz-Moreton, Hector
本文的目的是通过呼吸音检测病理。使用了ICBHI(国际生物医学和卫生信息学会议)基准。这个数据集由920个声音组成，其中810个是慢性病，75个是非慢性病，只有35个是健康人。由于超过88%的数据集样本来自同一类(慢性)，在确定数据集类别不平衡后，提出使用变分卷积自动编码器来生成新的标记数据和其他众所周知的过采样技术。一旦进行了预处理步骤，使用卷积神经网络(CNN)来将呼吸音分类为健康、慢性和非慢性疾病。此外，我们进行了一个更具挑战性的分类，试图区分不同类型的病理或健康：URTI、COPD、支扩、肺炎和毛细支气管炎。我们在三标签分类中获得了高达0.993 F分的结果，在更具挑战性的六类分类中获得了0.990 F分。
The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.