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Reseach Article

Detection and Classification of Abnormal Respiratory Sounds on a Resource-constraint Mobile Device

by Chinazunwa Uwaoma, Gunjan Mansingh
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 11
Year of Publication: 2014
Authors: Chinazunwa Uwaoma, Gunjan Mansingh
10.5120/ijais14-451265

Chinazunwa Uwaoma, Gunjan Mansingh . Detection and Classification of Abnormal Respiratory Sounds on a Resource-constraint Mobile Device. International Journal of Applied Information Systems. 7, 11 ( November 2014), 35-40. DOI=10.5120/ijais14-451265

@article{ 10.5120/ijais14-451265,
author = { Chinazunwa Uwaoma, Gunjan Mansingh },
title = { Detection and Classification of Abnormal Respiratory Sounds on a Resource-constraint Mobile Device },
journal = { International Journal of Applied Information Systems },
issue_date = { November 2014 },
volume = { 7 },
number = { 11 },
month = { November },
year = { 2014 },
issn = { 2249-0868 },
pages = { 35-40 },
numpages = {9},
url = { https://www.ijais.org/archives/volume7/number11/698-1265/ },
doi = { 10.5120/ijais14-451265 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:55:39.829628+05:30
%A Chinazunwa Uwaoma
%A Gunjan Mansingh
%T Detection and Classification of Abnormal Respiratory Sounds on a Resource-constraint Mobile Device
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 7
%N 11
%P 35-40
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Abnormal breath sounds like wheezes, crackles and stridor at times manifest similar morphologies and pathological features of lung airways obstruction. This may pose problems to proper diagnosis and evaluation of the underlying respiratory condition by human auscultation. In this study, the authors experimented with Time-Frequency threshold-dependent (TFTD) algorithm for detection and classification of breath sounds based on Smartphone. The TFTD algorithm computes important and distinct features of each breath sound using spectro-temporal analysis of recorded lung sounds which can enhance qualitative measurement and quantitative indexing of different respiratory sounds. Several algorithms which run exclusively on desktop computers have been developed for detecting and analyzing specific lung sounds such as wheezes. However, few attempts have been made to perform such analysis on portable devices like mobile phones due to computational complexities and high power consumption associated with the analyses. Our experimental results demonstrate that recent smartphones with improved computational capacity are able to provide comparative performance on analysis of respiratory signals. Furthermore, these phones can serve as convenient tools for measuring and detecting early signs of pulmonary disorders particularly at home and during ambulatory care services where conventional and specialized medical devices may not be accessible.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Respiratory sounds smartphone auscultation detection abnormal algorithm analysis computational capacity.