Detection of water in lung: algorithm study

Water retention is the build up of fluid in the body and its causes include congestive heart failure, severe chronic lung diseases and low protein levels in the blood. As the current lung water detection methods such as Computed Tomography (CT) scan, lung ultrasound and X-ray require expensive and b...

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Bibliographic Details
Main Author: Chew, Fang Yu
Other Authors: Ser Wee
Format: Final Year Project (FYP)
Language:English
Published: 2014
Subjects:
Online Access:http://hdl.handle.net/10356/61426
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author Chew, Fang Yu
author2 Ser Wee
author_facet Ser Wee
Chew, Fang Yu
author_sort Chew, Fang Yu
collection NTU
description Water retention is the build up of fluid in the body and its causes include congestive heart failure, severe chronic lung diseases and low protein levels in the blood. As the current lung water detection methods such as Computed Tomography (CT) scan, lung ultrasound and X-ray require expensive and bulky equipment, an alternative method, which is the acoustic-based technique, will be used to detect lung water retention. This method will be less expensive and the device will be more portable as compared to conventional detection methods and instruments. This project aims to create an algorithm that analyzes the audio signal recorded from patients with water retention in lungs and normal people whom do not have any water in their lungs. The lung sounds were provided at the start of the project and these samples were used for the various steps of the algorithm, which are feature extraction, classification and validation. From the spectral analysis of the lung sounds, there was a higher variation in magnitude of the signals for patients as compared to that of a normal person. In addition, it was observed that the classification accuracy rates were promising as they were both above 99%.
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spelling ntu-10356/614262023-07-07T16:03:33Z Detection of water in lung: algorithm study Chew, Fang Yu Ser Wee School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Water retention is the build up of fluid in the body and its causes include congestive heart failure, severe chronic lung diseases and low protein levels in the blood. As the current lung water detection methods such as Computed Tomography (CT) scan, lung ultrasound and X-ray require expensive and bulky equipment, an alternative method, which is the acoustic-based technique, will be used to detect lung water retention. This method will be less expensive and the device will be more portable as compared to conventional detection methods and instruments. This project aims to create an algorithm that analyzes the audio signal recorded from patients with water retention in lungs and normal people whom do not have any water in their lungs. The lung sounds were provided at the start of the project and these samples were used for the various steps of the algorithm, which are feature extraction, classification and validation. From the spectral analysis of the lung sounds, there was a higher variation in magnitude of the signals for patients as compared to that of a normal person. In addition, it was observed that the classification accuracy rates were promising as they were both above 99%. Bachelor of Engineering 2014-06-10T04:14:27Z 2014-06-10T04:14:27Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/61426 en Nanyang Technological University 47 p. application/pdf
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Chew, Fang Yu
Detection of water in lung: algorithm study
title Detection of water in lung: algorithm study
title_full Detection of water in lung: algorithm study
title_fullStr Detection of water in lung: algorithm study
title_full_unstemmed Detection of water in lung: algorithm study
title_short Detection of water in lung: algorithm study
title_sort detection of water in lung algorithm study
topic DRNTU::Engineering::Electrical and electronic engineering
url http://hdl.handle.net/10356/61426
work_keys_str_mv AT chewfangyu detectionofwaterinlungalgorithmstudy