Machine learning approach for automatic lungs sound diagnosis from pulmonary signals

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning approach for automatic lungs sound diagnosis from pulmonary signals

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine learning approach for automatic lungs sound diagnosis from pulmonary signals

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

44th International Conference on Telecommunications and Signal Processing (TSP)

ISBN

978-1-6654-2933-7

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

366-371

Název nakladatele

IEEE

Místo vydání

Virtual Conference

Místo konání akce

Brno

Datum konání akce

26. 7. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000701604600078