Detecting Respiratory Pathologies Using Convolutional Neural Networks
  and Variational Autoencoders for Unbalancing Data
María Teresa García-Ordás José Alberto Benítez-Andrades Isaías García-Rodríguez Carmen Benavides Héctor Alaiz-Moretón
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.