Distinctive features for classification of respiratory sounds between normal and crackles 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 d...

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Main Authors:	Mohd Johari, Nabila Husna, Abd Malik, Noreha, Sidek, Khairul Azami
Format:	Conference or Workshop Item
Language:	English English
Published:	IEEE 2018
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/7/67988%20Distinctive%20Features%20for%20Classification%20of.pdf http://irep.iium.edu.my/67988/13/67988%20Distinctive%20Features%20for%20Classification%20_scopus.pdf

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recordtype	eprints
spelling	iium-679882019-08-17T03:50:40Z http://irep.iium.edu.my/67988/ Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients Mohd Johari, Nabila Husna Abd 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, Mel-frequency Cepstral Coefficient (MFCC) is 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 result shows that the first three statistical values of SD of coefficients provide distinctive feature between normal and crackles respiratory sounds. Hence, MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool. IEEE 2018-11 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/67988/7/67988%20Distinctive%20Features%20for%20Classification%20of.pdf application/pdf en http://irep.iium.edu.my/67988/13/67988%20Distinctive%20Features%20for%20Classification%20_scopus.pdf Mohd Johari, Nabila Husna and Abd Malik, Noreha and Sidek, Khairul Azami (2018) Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients. In: 2018 7th International Conference on Computer and Communication Engineering (ICCCE), 19th-20th September 2018, Kuala Lumpur. https://ieeexplore.ieee.org/document/8539305 10.1109/ICCCE.2018.8539305
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 Abd Malik, Noreha Sidek, Khairul Azami Distinctive features for classification of respiratory sounds between normal and crackles 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, Mel-frequency Cepstral Coefficient (MFCC) is 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 result shows that the first three statistical values of SD of coefficients provide distinctive feature between normal and crackles respiratory sounds. Hence, MFCCs can be used as feature extraction method of respiratory sounds to classify between normal and crackles as screening and diagnostic tool.
format	Conference or Workshop Item
author	Mohd Johari, Nabila Husna Abd Malik, Noreha Sidek, Khairul Azami
author_facet	Mohd Johari, Nabila Husna Abd Malik, Noreha Sidek, Khairul Azami
author_sort	Mohd Johari, Nabila Husna
title	Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients
title_short	Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients
title_full	Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients
title_fullStr	Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients
title_full_unstemmed	Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients
title_sort	distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients
publisher	IEEE
publishDate	2018
url	http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/ http://irep.iium.edu.my/67988/7/67988%20Distinctive%20Features%20for%20Classification%20of.pdf http://irep.iium.edu.my/67988/13/67988%20Distinctive%20Features%20for%20Classification%20_scopus.pdf
first_indexed	2023-09-18T21:36:31Z
last_indexed	2023-09-18T21:36:31Z
_version_	1777412857638420480

Distinctive features for classification of respiratory sounds between normal and crackles using cepstral coefficients

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