Features extraction of heart sounds using timefrequency distribution and mel-frequency cepstrum coefficient

Heart sounds analysis can provide lots of information about heart condition whether it is normal or abnormal. Heart sounds signals are time-varying signals where they exhibit some degree of non-stationary. Due to these characteristics, therefore, two techniques have been proposed to analyze them. Th...

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Main Author:	Mohamed, Masnani
Format:	Thesis
Language:	English
Published:	2006
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/2724/1/MasnaniMohamedMFKE2006.pdf
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id	my-utm-ep.2724
record_format	uketd_dc
spelling	my-utm-ep.27242018-09-17T03:03:04Z Features extraction of heart sounds using timefrequency distribution and mel-frequency cepstrum coefficient 2006-05 Mohamed, Masnani TK Electrical engineering. Electronics Nuclear engineering Heart sounds analysis can provide lots of information about heart condition whether it is normal or abnormal. Heart sounds signals are time-varying signals where they exhibit some degree of non-stationary. Due to these characteristics, therefore, two techniques have been proposed to analyze them. The first technique is the Time-Frequency Distribution using B-Distribution, used to resolve signalâ€™s components in the time-frequency domain and specifies the frequency components of the signal that changing over time. Another proposed technique is the Mel- Frequency Cepstrum Coefficient, used to obtain the cepstrums coefficients by resolving signalâ€™s components in the frequency domain. An experiment is presented to extract features of heart sounds using both mentioned techniques and compare their performances. Both techniques are discussed in details and tested against ideal simulations of 50 heart sound signals including normal and abnormal signals. All simulations are done using Matlab software except for MFCC where it has used the Microsoft Visual C++ software. A brief description of SVD is included to the technique using time-frequency distribution. Also, a brief description of Neural Network is used to verify and to compare the performances results of the two techniques with regard to the values of hidden node, learning rate and momentum coefficient. The results showed that performance of the TFD can be achieved up to 90% whereas MFCC is only 80%. Therefore, the TFD technique is chosen as the best technique to analyze and to extract features of the non-stationary signals such as the heart sounds signals 2006-05 Thesis http://eprints.utm.my/id/eprint/2724/ http://eprints.utm.my/id/eprint/2724/1/MasnaniMohamedMFKE2006.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Mohamed, Masnani Features extraction of heart sounds using timefrequency distribution and mel-frequency cepstrum coefficient
description	Heart sounds analysis can provide lots of information about heart condition whether it is normal or abnormal. Heart sounds signals are time-varying signals where they exhibit some degree of non-stationary. Due to these characteristics, therefore, two techniques have been proposed to analyze them. The first technique is the Time-Frequency Distribution using B-Distribution, used to resolve signalâ€™s components in the time-frequency domain and specifies the frequency components of the signal that changing over time. Another proposed technique is the Mel- Frequency Cepstrum Coefficient, used to obtain the cepstrums coefficients by resolving signalâ€™s components in the frequency domain. An experiment is presented to extract features of heart sounds using both mentioned techniques and compare their performances. Both techniques are discussed in details and tested against ideal simulations of 50 heart sound signals including normal and abnormal signals. All simulations are done using Matlab software except for MFCC where it has used the Microsoft Visual C++ software. A brief description of SVD is included to the technique using time-frequency distribution. Also, a brief description of Neural Network is used to verify and to compare the performances results of the two techniques with regard to the values of hidden node, learning rate and momentum coefficient. The results showed that performance of the TFD can be achieved up to 90% whereas MFCC is only 80%. Therefore, the TFD technique is chosen as the best technique to analyze and to extract features of the non-stationary signals such as the heart sounds signals
format	Thesis
qualification_level	Master's degree
author	Mohamed, Masnani
author_facet	Mohamed, Masnani
author_sort	Mohamed, Masnani
title	Features extraction of heart sounds using timefrequency distribution and mel-frequency cepstrum coefficient
title_short	Features extraction of heart sounds using timefrequency distribution and mel-frequency cepstrum coefficient
title_full	Features extraction of heart sounds using timefrequency distribution and mel-frequency cepstrum coefficient
title_fullStr	Features extraction of heart sounds using timefrequency distribution and mel-frequency cepstrum coefficient
title_full_unstemmed	Features extraction of heart sounds using timefrequency distribution and mel-frequency cepstrum coefficient
title_sort	features extraction of heart sounds using timefrequency distribution and mel-frequency cepstrum coefficient
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2006
url	http://eprints.utm.my/id/eprint/2724/1/MasnaniMohamedMFKE2006.pdf
_version_	1747814428026339328

Features extraction of heart sounds using timefrequency distribution and mel-frequency cepstrum coefficient

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