Distinctive features for normal and crackles respiratory sounds using cepstral coefficients

Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detect...

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Main Authors:	Mohd Johari, Nabila Husna, Abdul Malik, Noreha, Sidek, Khairul Azami
Format:	Article
Language:	English English
Published:	Universitas Ahmad Dahlan 2019
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/73673/ http://irep.iium.edu.my/73673/ http://irep.iium.edu.my/73673/ http://irep.iium.edu.my/73673/1/73673_Distinctive%20features%20for%20normal.pdf http://irep.iium.edu.my/73673/7/73673_Distinctive%20features%20for%20normal%20and%20crackles%20respiratory%20sounds%20using%20cepstral%20coefficients_Scopus.pdf

id	iium-73673
recordtype	eprints
spelling	iium-736732019-11-24T15:35:42Z http://irep.iium.edu.my/73673/ Distinctive features for normal and crackles respiratory sounds using cepstral coefficients Mohd Johari, Nabila Husna Abdul Malik, Noreha Sidek, Khairul Azami TK7885 Computer engineering Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard deviation (SD) of cepstral based coefficients it can differentiate between crackles and normal sounds. The statistical computations of the cepstral coefficient of LPCC and MFCC show that the mean LPCC except for the third coefficient and first three statistical coefficient values of MFCC’s SD provide distinctive feature between normal and crackles respiratory sounds. Hence, LPCCs and MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool. Universitas Ahmad Dahlan 2019-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/73673/1/73673_Distinctive%20features%20for%20normal.pdf application/pdf en http://irep.iium.edu.my/73673/7/73673_Distinctive%20features%20for%20normal%20and%20crackles%20respiratory%20sounds%20using%20cepstral%20coefficients_Scopus.pdf Mohd Johari, Nabila Husna and Abdul Malik, Noreha and Sidek, Khairul Azami (2019) Distinctive features for normal and crackles respiratory sounds using cepstral coefficients. Bulletin of Electrical Engineering and Informatics, 8 (3). pp. 875-881. ISSN 2302-9285 http://journal.portalgaruda.org/index.php/EEI/article/view/1517 10.11591/eei.v8i3.1517
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Mohd Johari, Nabila Husna Abdul Malik, Noreha Sidek, Khairul Azami Distinctive features for normal and crackles respiratory sounds using cepstral coefficients
description	Classification of respiratory sounds between normal and abnormal is very crucial for screening and diagnosis purposes. Lung associated diseases can be detected through this technique. With the advancement of computerized auscultation technology, the adventitious sounds such as crackles can be detected and therefore diagnostic test can be performed earlier. In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. By using statistical computation such as mean and standard deviation (SD) of cepstral based coefficients it can differentiate between crackles and normal sounds. The statistical computations of the cepstral coefficient of LPCC and MFCC show that the mean LPCC except for the third coefficient and first three statistical coefficient values of MFCC’s SD provide distinctive feature between normal and crackles respiratory sounds. Hence, LPCCs and MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool.
format	Article
author	Mohd Johari, Nabila Husna Abdul Malik, Noreha Sidek, Khairul Azami
author_facet	Mohd Johari, Nabila Husna Abdul Malik, Noreha Sidek, Khairul Azami
author_sort	Mohd Johari, Nabila Husna
title	Distinctive features for normal and crackles respiratory sounds using cepstral coefficients
title_short	Distinctive features for normal and crackles respiratory sounds using cepstral coefficients
title_full	Distinctive features for normal and crackles respiratory sounds using cepstral coefficients
title_fullStr	Distinctive features for normal and crackles respiratory sounds using cepstral coefficients
title_full_unstemmed	Distinctive features for normal and crackles respiratory sounds using cepstral coefficients
title_sort	distinctive features for normal and crackles respiratory sounds using cepstral coefficients
publisher	Universitas Ahmad Dahlan
publishDate	2019
url	http://irep.iium.edu.my/73673/ http://irep.iium.edu.my/73673/ http://irep.iium.edu.my/73673/ http://irep.iium.edu.my/73673/1/73673_Distinctive%20features%20for%20normal.pdf http://irep.iium.edu.my/73673/7/73673_Distinctive%20features%20for%20normal%20and%20crackles%20respiratory%20sounds%20using%20cepstral%20coefficients_Scopus.pdf
first_indexed	2023-09-18T21:44:27Z
last_indexed	2023-09-18T21:44:27Z
_version_	1777413355944804352

Distinctive features for normal and crackles respiratory sounds using cepstral coefficients

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