Machine learning approach for automatic lungs sound diagnosis from pulmonary signals

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU141505" target="_blank" >RIV/00216305:26220/21:PU141505 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/TSP52935.2021.9522663" target="_blank" >http://dx.doi.org/10.1109/TSP52935.2021.9522663</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP52935.2021.9522663" target="_blank" >10.1109/TSP52935.2021.9522663</a>

Result language
angličtina
Original language name
Machine learning approach for automatic lungs sound diagnosis from pulmonary signals
Original language description
Chronic Respiratory Diseases (CRDs) are the most common diseases that affect people in today’s world. In COVID 19 pandemic many people are suffering from different types of respiratory diseases. There is a shortage of medical professionals and hence there is a requirement of artificial intelligence-based tools for automatic diagnosis of pulmonary diseases in the lungs. This paper presents a machine learning-based automatic classification method for the diagnosis of multiple pulmonary diseases from lung sounds. This work uses comprehensive lung sound categories labeled by a medical professional for use in machine learning-based classification. The proposed work uses four machine-learning classifiers (SVM, KNN, Naïve Bayes, and ANN) for the different discriminant features of lung sounds such as wheezing sound that can be used for diagnosis of asthma. For the detection of multiple lung sound in a noisy environment, data augmentation is used in training data and then trained the model where ANN using 5-fold cross-validation gives the average accuracy of 95.6%. The proposed method has low time complexity, is robust and non-invasive making it ideal for real-time applications to diagnose pulmonary diseases.
Czech name
—
Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
44th International Conference on Telecommunications and Signal Processing (TSP)
ISBN
978-1-6654-2933-7
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
366-371
Publisher name
IEEE
Place of publication
Virtual Conference
Event location
Brno
Event date
Jul 26, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000701604600078

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