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Classification of respiratory pathology from pulmonary acoustic signals based on respiratory cycle segmentation and two-stage classification

Doctor of Philosophy in Mechatronic Engineering

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Bibliographic Details
Main Author: Rajkumar, Palaniappan
Other Authors: Kenneth, Sundaraj, Assoc. Prof. Dr.
Format: Thesis
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2015
Subjects:
Auscultation
Respiratory
Internal sounds
Computerized decision support system (CDSS)
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78266
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spelling my.unimap-782662023-04-04T00:31:53Z Classification of respiratory pathology from pulmonary acoustic signals based on respiratory cycle segmentation and two-stage classification Rajkumar, Palaniappan Kenneth, Sundaraj, Assoc. Prof. Dr. Auscultation Respiratory Internal sounds Computerized decision support system (CDSS) Doctor of Philosophy in Mechatronic Engineering Auscultation is the process of listening to the internal sounds of the body using a stethoscope. This process provides vital information on the present state of the internal organs, such as the heart, lungs and the gastrointestinal system. Auscultation is subjective and prone to be not reliable. However computerized respiratory sound analysis is more effective and reliable. This thesis discusses the development of a computerized decision support system (CDSS) to detect respiratory pathology using pulmonary acoustic signals. The pulmonary acoustics signals were collected from 72 subjects to develop the CDSS. In order to develop the CDSS tool, three different methodological frameworks were proposed to determine the most effective classification of respiratory pathology. The recorded pulmonary acoustics signals were filtered to remove noise and other artifacts followed by respiratory cycle segmentation. In this work, the respiratory cycle segmentation is performed by using Fuzzy Inference system. Parametric (Mel-frequency cepstral coefficients (MFCC) and Auto-regressive model (AR)) and Nonparametric (wavelet packet transform (WPT) and Stockwell transform (ST)) representations of features were extracted. The features extracted were dimensionally reduced using principal component analysis and a statistical analysis was performed to determine the significance level of the feature vector using One-way ANOVA. Observations showed that the extracted features were statistically significant with p < 0.05. In the classification stage various nonlinear classifiers such as k-nearest neighbor (k-nn), support vector machines (SVM) and extreme learning machine (ELM) were implemented to classify the respiratory pathology from respiratory sounds. In the classification, extreme learning machine performed better than k-nn and support vector machine classifier for all the frameworks. Experimental results showed that ST based feature extraction performed well with ELM classifier with third framework. The ST based features and ELM classifier with third framework was validated using a new set of data comprising of 48 subjects and the system was found to be reliable with mean classification accuracy of 96.63%, 97.57% and 98.48% for classifying (normal, continuous lung sounds and discontinuous lung sounds), (wheeze and rhonchi) and (fine crackles and coarse crackles) respectively. After successful validation a CDSS tool was developed using the ST based features and ELM classifier with third framework 2015 2023-04-04T00:30:13Z 2023-04-04T00:30:13Z Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78266 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Mechatronic Engineering
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Auscultation
Respiratory
Internal sounds
Computerized decision support system (CDSS)
spellingShingle Auscultation
Respiratory
Internal sounds
Computerized decision support system (CDSS)
Rajkumar, Palaniappan
Classification of respiratory pathology from pulmonary acoustic signals based on respiratory cycle segmentation and two-stage classification
description Doctor of Philosophy in Mechatronic Engineering
author2 Kenneth, Sundaraj, Assoc. Prof. Dr.
author_facet Kenneth, Sundaraj, Assoc. Prof. Dr.
Rajkumar, Palaniappan
format Thesis
author Rajkumar, Palaniappan
author_sort Rajkumar, Palaniappan
title Classification of respiratory pathology from pulmonary acoustic signals based on respiratory cycle segmentation and two-stage classification
title_short Classification of respiratory pathology from pulmonary acoustic signals based on respiratory cycle segmentation and two-stage classification
title_full Classification of respiratory pathology from pulmonary acoustic signals based on respiratory cycle segmentation and two-stage classification
title_fullStr Classification of respiratory pathology from pulmonary acoustic signals based on respiratory cycle segmentation and two-stage classification
title_full_unstemmed Classification of respiratory pathology from pulmonary acoustic signals based on respiratory cycle segmentation and two-stage classification
title_sort classification of respiratory pathology from pulmonary acoustic signals based on respiratory cycle segmentation and two-stage classification
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2015
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78266
_version_ 1772813069698727936
score 13.214268

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