Next Article in Journal
An Efficient, Anonymous and Robust Authentication Scheme for Smart Home Environments
Next Article in Special Issue
Feasibility of Social-Network-Based eHealth Intervention on the Improvement of Healthy Habits among Children
Previous Article in Journal
An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology
Open AccessArticle

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

1
SECOMUCI Research Groups, Escuela de Ingenierías Industrial e Informática, Universidad de León, Campus de Vegazana s/n, C.P. 24071 León, Spain
2
SALBIS Research Group, Department of Electric, Systems and Automatics Engineering, Universidad de León, Campus of Vegazana s/n, 24071 León, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 1214; https://doi.org/10.3390/s20041214
Received: 20 January 2020 / Revised: 16 February 2020 / Accepted: 21 February 2020 / Published: 22 February 2020
(This article belongs to the Special Issue Sensor and Systems Evaluation for Telemedicine and eHealth)
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. View Full-Text
Keywords: CNN; variational autoencoder; respiratory; lungs; pathologies CNN; variational autoencoder; respiratory; lungs; pathologies
Show Figures

Figure 1

MDPI and ACS Style

García-Ordás, M.T.; Benítez-Andrades, J.A.; García-Rodríguez, I.; Benavides, C.; Alaiz-Moretón, H. Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data. Sensors 2020, 20, 1214. https://doi.org/10.3390/s20041214

AMA Style

García-Ordás MT, Benítez-Andrades JA, García-Rodríguez I, Benavides C, Alaiz-Moretón H. Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data. Sensors. 2020; 20(4):1214. https://doi.org/10.3390/s20041214

Chicago/Turabian Style

García-Ordás, María T.; Benítez-Andrades, José A.; García-Rodríguez, Isaías; Benavides, Carmen; Alaiz-Moretón, Héctor. 2020. "Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data" Sensors 20, no. 4: 1214. https://doi.org/10.3390/s20041214

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop