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Authors: Chinazunwa Uwaoma and Gunjan Mansingh

Affiliation: The University of the West Indies, Jamaica

ISBN: 978-989-758-263-9

Keyword(s): Smartphone, Machine Learning, Algorithms, Respiratory, Sound Analysis, Classification, Symptoms.

Related Ontology Subjects/Areas/Topics: Biological Inspired Sensors ; Computer Vision, Visualization and Computer Graphics ; Human-Machine Interfaces ; Image and Video Analysis ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Machine Learning in Control Applications ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control ; Time-Frequency Analysis

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.

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Paper citation in several formats:
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

@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},
}

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

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