On Smartphone-based Discrimination of Pathological Respiratory Sounds with Similar Acoustic Properties using Machine Learning Algorithms

Chinazunwa Uwaoma, Gunjan Mansingh

2017

Abstract

This paper explores the capabilities of mobile phones to distinguish sound-related symptoms of respiratory conditions using machine learning algorithms. The classification tool is modeled after some standard set of temporal and spectral features used in vocal and lung sound analysis. These features are extracted from recorded sounds and then fed into machine learning algorithms to train the mobile system. Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbour (kNN) classifiers were evaluated with an overall accuracy of 86.7%, 75.8%, and 88.9% respectively. The appreciable performance of these classifiers on a mobile phone shows smartphone as an alternate tool for recognition and discrimination of respiratory symptoms in real-time scenarios.

Paper Citation

in Harvard Style

Uwaoma C. and Mansingh G. (2017). On Smartphone-based Discrimination of Pathological Respiratory Sounds with Similar Acoustic Properties using Machine Learning Algorithms . In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-263-9, pages 422-430. DOI: 10.5220/0006404604220430

in Bibtex Style

@conference{icinco17,
author={Chinazunwa Uwaoma and Gunjan Mansingh},
title={On Smartphone-based Discrimination of Pathological Respiratory Sounds with Similar Acoustic Properties using Machine Learning Algorithms},
booktitle={Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2017},
pages={422-430},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006404604220430},
isbn={978-989-758-263-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - On Smartphone-based Discrimination of Pathological Respiratory Sounds with Similar Acoustic Properties using Machine Learning Algorithms
SN - 978-989-758-263-9
AU - Uwaoma C.
AU - Mansingh G.
PY - 2017
SP - 422
EP - 430
DO - 10.5220/0006404604220430