SECOMUCI Research Groups, Escuela de Ingenierías Industrial e Informática, Universidad de León, Campus de Vegazana s/n, C.P. 24071 León, Spain.
SALBIS Research Group, Department of Electric, Systems and Automatics Engineering, University of León, Campus of Vegazana s/n, 24071 León, Spain.
Sensors (Basel). 2020 Feb 22;20(4):1214. doi: 10.3390/s20041214.
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.
本文旨在通过呼吸音检测病理。使用了 ICBHI（国际生物医学和健康信息学会议）基准数据集。该数据集由 920 个声音组成，其中 810 个为慢性疾病声音，75 个为非慢性疾病声音，只有 35 个为健康个体的声音。由于该数据集的样本中超过 88%来自同一类别（慢性），因此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生成新的标记数据和其他知名的过采样技术。在完成预处理步骤后，使用卷积神经网络（CNN）将呼吸声分类为健康、慢性和非慢性疾病。此外，我们进行了更具挑战性的分类，试图区分不同类型的病理或健康：上呼吸道感染、COPD、支气管扩张、肺炎和细支气管炎。在三标签分类中，我们达到了高达 0.993 的 F 分数，在更具挑战性的六标签分类中达到了 0.990 的 F 分数。
