An audio classification system for detecting pulmonary edema

Pulmonary edema is a condition where water engorges the alveolar beds. The water is pushed into air spaces and thus reduces normal oxygen movement. It can cause hemoptysis, difficulty in breathing, shorted of breath, and gurgling and wheezing sounds during breathing. It can be caused by congestive h...

Full description

Saved in:
Bibliographic Details
Main Author: Hong, Kah Jun
Other Authors: Ser Wee
Format: Theses and Dissertations
Language:English
Published: 2019
Subjects:
Online Access:https://hdl.handle.net/10356/90282
http://hdl.handle.net/10220/48518
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-90282
record_format dspace
spelling sg-ntu-dr.10356-902822023-07-04T16:21:43Z An audio classification system for detecting pulmonary edema Hong, Kah Jun Ser Wee School of Electrical and Electronic Engineering Slim Essid DRNTU::Engineering::Electrical and electronic engineering Pulmonary edema is a condition where water engorges the alveolar beds. The water is pushed into air spaces and thus reduces normal oxygen movement. It can cause hemoptysis, difficulty in breathing, shorted of breath, and gurgling and wheezing sounds during breathing. It can be caused by congestive heart failure, kidney failure, high altitude exposure, lung damage, and major injuries. Currently, physicians will carry out physical examinations before performing X-rays, CT-scans and electrocardiograms. Physicians mainly use auscultations to check for abnormal heart sounds, crackles, increased heart rate and rapid breathing. Most patients with pulmonary edema must face the disease and its implications for the rest of their lives. They must visit the physician for a check-up whenever they show symptoms, and if left untreated, they could suffer suffocation. These constant trips to the physician can add stress, worry and inconvenience. Physicians also in turn face a greater workload. This thesis proposes new insights and methods to detect pulmonary edema using auscultation recordings. The first contribution makes use of feature engineering to gain new insights into the characteristics of these audio recordings. Empirical mode decomposition is used to improve the classification performance. In the first contribution, it is shown that conventional feature selection algorithms are not designed to handle external interfering hospital noises. In the second contribution, improvements are made on the robustness with the introduction of a new system. The second contribution uses non-negative matrix factorization (NMF) to develop a robust audio classification system to detect pulmonary edema. A study was done to compare feature engineering approaches with feature selection techniques against NMF. Different NMF schemes were investigated and compared with Principal Component Analysis. Background noise collected from hospitals and speech from a speech corpus database were used to simulate noisy data. The system was then tested using noisy data. NMF was also used as a signal enhancement tool, which improved the classification scores. Contributions in the third chapter are divided into two main parts. Inspired by the analysis of spectrograms of breath recordings, the first section investigates the use of Latent SSVM to show that breath sounds contain latent information that could potentially be exploited. To further improve the proposed NMF system, the second part of the contribution uses NMF to develop a cascading dictionary learning algorithm that allows the use of sample subsets to increase classification accuracy. The SVM classifier is used to group subsets of data and calculate dictionaries on each subset of data. These subsets can be thought of as latent variables that have been grouped into these subsets. The method is described and we compare results with benchmarks, including results from the previous chapter. In conclusion, this thesis focuses on the automatic classification of pulmonary edema with the use of breath recordings through the chest wall. In particular, feature engineering was used to gain new insights. NMF was next used to develop a robust classification system. Lastly, Latent SSVM was used to show that breath sounds contain latent information that could be exploited. A cascading dictionary learning algorithm was proposed for that purpose and improved classification results in noisy conditions. Doctor of Philosophy 2019-06-03T06:30:17Z 2019-12-06T17:44:44Z 2019-06-03T06:30:17Z 2019-12-06T17:44:44Z 2019 Thesis Hong, K. J. (2019). Thermal failure of carbon nanostructures. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/90282 http://hdl.handle.net/10220/48518 10.32657/10220/48518 en 106 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Hong, Kah Jun
An audio classification system for detecting pulmonary edema
description Pulmonary edema is a condition where water engorges the alveolar beds. The water is pushed into air spaces and thus reduces normal oxygen movement. It can cause hemoptysis, difficulty in breathing, shorted of breath, and gurgling and wheezing sounds during breathing. It can be caused by congestive heart failure, kidney failure, high altitude exposure, lung damage, and major injuries. Currently, physicians will carry out physical examinations before performing X-rays, CT-scans and electrocardiograms. Physicians mainly use auscultations to check for abnormal heart sounds, crackles, increased heart rate and rapid breathing. Most patients with pulmonary edema must face the disease and its implications for the rest of their lives. They must visit the physician for a check-up whenever they show symptoms, and if left untreated, they could suffer suffocation. These constant trips to the physician can add stress, worry and inconvenience. Physicians also in turn face a greater workload. This thesis proposes new insights and methods to detect pulmonary edema using auscultation recordings. The first contribution makes use of feature engineering to gain new insights into the characteristics of these audio recordings. Empirical mode decomposition is used to improve the classification performance. In the first contribution, it is shown that conventional feature selection algorithms are not designed to handle external interfering hospital noises. In the second contribution, improvements are made on the robustness with the introduction of a new system. The second contribution uses non-negative matrix factorization (NMF) to develop a robust audio classification system to detect pulmonary edema. A study was done to compare feature engineering approaches with feature selection techniques against NMF. Different NMF schemes were investigated and compared with Principal Component Analysis. Background noise collected from hospitals and speech from a speech corpus database were used to simulate noisy data. The system was then tested using noisy data. NMF was also used as a signal enhancement tool, which improved the classification scores. Contributions in the third chapter are divided into two main parts. Inspired by the analysis of spectrograms of breath recordings, the first section investigates the use of Latent SSVM to show that breath sounds contain latent information that could potentially be exploited. To further improve the proposed NMF system, the second part of the contribution uses NMF to develop a cascading dictionary learning algorithm that allows the use of sample subsets to increase classification accuracy. The SVM classifier is used to group subsets of data and calculate dictionaries on each subset of data. These subsets can be thought of as latent variables that have been grouped into these subsets. The method is described and we compare results with benchmarks, including results from the previous chapter. In conclusion, this thesis focuses on the automatic classification of pulmonary edema with the use of breath recordings through the chest wall. In particular, feature engineering was used to gain new insights. NMF was next used to develop a robust classification system. Lastly, Latent SSVM was used to show that breath sounds contain latent information that could be exploited. A cascading dictionary learning algorithm was proposed for that purpose and improved classification results in noisy conditions.
author2 Ser Wee
author_facet Ser Wee
Hong, Kah Jun
format Theses and Dissertations
author Hong, Kah Jun
author_sort Hong, Kah Jun
title An audio classification system for detecting pulmonary edema
title_short An audio classification system for detecting pulmonary edema
title_full An audio classification system for detecting pulmonary edema
title_fullStr An audio classification system for detecting pulmonary edema
title_full_unstemmed An audio classification system for detecting pulmonary edema
title_sort audio classification system for detecting pulmonary edema
publishDate 2019
url https://hdl.handle.net/10356/90282
http://hdl.handle.net/10220/48518
_version_ 1772828150449831936