Publication detail

Machine learning approach for automatic lungs sound diagnosis from pulmonary signals

MYŠKA, V. BURGET, R.

Original Title

Machine learning approach for automatic lungs sound diagnosis from pulmonary signals

Type

conference paper

Language

English

Original Abstract

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.

Keywords

Artificial Neural Network; Machine Learning;Pulmonary disease;Respiratory sounds classification

Authors

MYŠKA, V.; BURGET, R.

Released

26. 7. 2021

Publisher

IEEE

Location

Virtual Conference

ISBN

978-1-6654-2933-7

Book

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

Pages from

366

Pages to

371

Pages count

6

BibTex

@inproceedings{BUT172368,
  author="Vojtěch {Myška} and Radim {Burget}",
  title="Machine learning approach for automatic lungs sound diagnosis from pulmonary signals",
  booktitle="44th International Conference on Telecommunications and Signal Processing (TSP)",
  year="2021",
  pages="366--371",
  publisher="IEEE",
  address="Virtual Conference",
  doi="10.1109/TSP52935.2021.9522663",
  isbn="978-1-6654-2933-7"
}