Imperial College London
Alternate

Dr Syed Anas Imtiaz

Faculty of Engineering, Department of Electrical and Electronic Engineering

Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 6297anas.imtiaz Website

 
 
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Location

 

907Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Kok:2019:10.1109/EMBC.2019.8857154,
author = {Kok, XH and Anas, Imtiaz S and Rodriguez-Villegas, E},
doi = {10.1109/EMBC.2019.8857154},
pages = {2589--2592},
publisher = {IEEE},
title = {A novel method for automatic identification of respiratory disease from acoustic recordings.},
url = {http://dx.doi.org/10.1109/EMBC.2019.8857154},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - This paper evaluates the use of breath sound recordings to automatically determine the respiratory health status of a subject. A number of features were investigated and Wilcoxon Rank Sum statistical test was used to determine the significance of the extracted features. The significant features were then passed to a feature selection algorithm based on mutual information, to determine the combination of features that provided minimal redundancy and maximum relevance. The algorithm was tested on a publicly accessible respiratory sounds database. With the testing dataset, the trained classifier achieved accuracy of 87.1%, sensitivity of 86.8% and specificity of 93.6%. These are promising results showing the possibility of determining the presence or absence of respiratory disease using breath sounds recordings.
AU  - Kok,XH
AU  - Anas,Imtiaz S
AU  - Rodriguez-Villegas,E
DO  - 10.1109/EMBC.2019.8857154
EP  - 2592
PB  - IEEE
PY  - 2019///
SN  - 1557-170X
SP  - 2589
TI  - A novel method for automatic identification of respiratory disease from acoustic recordings.
UR  - http://dx.doi.org/10.1109/EMBC.2019.8857154
UR  - https://www.ncbi.nlm.nih.gov/pubmed/31946426
UR  - https://ieeexplore.ieee.org/document/8857154
UR  - http://hdl.handle.net/10044/1/79731
ER  -
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