EST

Call for paper
April Edition 2017

International Journal of Applied Information Systems solicits high quality original research papers for the
March 15, 2017
April 2017 Edition of the journal.
The last date of research paper submission is
March 15, 2017
SUBMIT YOUR PAPER

Number 11

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

journal image
 Download
1026

Export

  • Chinazunwa Uwaoma and Gunjan Mansingh 2014. 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=http://dx.doi.org/10.5120/ijais451265 
  • @article{10.5120/ijais2017451568,
author = {Chinazunwa Uwaoma and 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 = {},
month = {November},
year = {2014},
issn = {},
pages = {35-40},
numpages = {},
url = {/archives/volume7/number11/698-1265},
doi = { 10.5120/ijais14-451265},
publisher = { xA9 2013 by IJAIS Journal},
address = {}
}

  • %1 451265
%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
%@ 
%V 7
%N 
%P 35-40
%D 2014
%I  xA9 2013 by IJAIS Journal

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

  1. Shaharum, S. Sundaraj, K. , and Palaniappan, R. 2012. "A survey on automated wheeze detection systems for asthmatic patients. " Bosnian Journal of Basic Med. Sci. vol. 12, no. 4, pp. 249-255
  2. Lin, B. , and Yen, T. 2014. "An FPGA-Based Rapid Wheezing Detection System. " International journal of environmental research and public health, vol. 11, no. 2, pp. 1573-1593.
  3. Taplidou, S. , and Hadjileontiadis, L. J. 2007. "Wheeze detection based on time-frequency analysis of breath sounds," Computers in biology and medicine, vol. 37, no. 8, pp. 1073-1083.
  4. Emrani, S. , and Krim, H. 2013. "Wheeze Detection and Location using Spectro-temporal Analysis of Lung Sounds. " In Proceedings of 29th Southern Biomedical Engineering Conference (SBEC), IEEE, 2013, pp. 37-38.
  5. Yu, C. , Tsai, T. , Huang, S. , and Lin, C. 2013. "Soft Stethoscope for Detecting Asthma Wheeze in Young Children. " Sensors, vol. 13, no. 6, pp. 7399-7413.
  6. Oletic, D. , Arsenali, B. , and Bilas, V. 2014. "Low-Power Wearable Respiratory Sound Sensing. " Sensors, vol. 14 no. 4, pp. 6535-6566.
  7. Uwaoma, C. , and Mansingh, G. 2014. "Using Embedded Sensors in Smartphones to Monitor and Detect Early Symptoms of Exercise-induced Asthma. " In Proceedings of 3rd Intl. Conf. on Sensor Networks, SENSORNETS 2014, pp. 145-150.
  8. Reichert, S. , Gass, R. , Brandt, C. , and AndrxE8, E. 2008. "Analysis of respiratory sounds: state of the art. " Clinical medicine Circulatory, respiratory and pulmonary medicine, vol. 2, pp. 45 -58.
  9. R. A. L. E Lung Sounds. (n. d) Retrieved from: http://www. rale. ca.

Keywords

Respiratory sounds, smartphone, auscultation, detection, abnormal, algorithm, analysis,computational capacity.

Index Terms

Computer Science
Information Sciences