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Artificial Intelligence Techniques Used for Wheeze Sounds Analysis: Review

Wheezes are acoustic, adventitious, continues and high pitch pulmonary sounds produce due to airway obstruction, these sounds mostly exist in pneumonia and asthma patients. Artificial intelligence techniques have been extensively used for wheeze sound analysis to diagnose patient. The available lite...

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Main Authors:	Nabi, F. G., Sundaraj, K., Kiang, L. C., Palaniappan, R., Sundaraj, S., Ahamed, Nizam Uddin
Other Authors:	Fatimah, Ibrahim
Format:	Book Section
Language:	English English
Published:	Springer 2017
Subjects:	TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/17458/1/book%20chapter2.pdf http://umpir.ump.edu.my/id/eprint/17458/7/5.%20Artificial%20Intelligence%20Techniques%20Used%20for%20Wheeze%20Sounds%20Analysis%20Review.pdf http://umpir.ump.edu.my/id/eprint/17458/ http://dx.doi.org/0.1007/978-981-10-3737-5_8
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id	my.ump.umpir.17458
record_format	eprints
spelling	my.ump.umpir.174582018-05-02T02:52:58Z http://umpir.ump.edu.my/id/eprint/17458/ Artificial Intelligence Techniques Used for Wheeze Sounds Analysis: Review Nabi, F. G. Sundaraj, K. Kiang, L. C. Palaniappan, R. Sundaraj, S. Ahamed, Nizam Uddin TS Manufactures Wheezes are acoustic, adventitious, continues and high pitch pulmonary sounds produce due to airway obstruction, these sounds mostly exist in pneumonia and asthma patients. Artificial intelligence techniques have been extensively used for wheeze sound analysis to diagnose patient. The available literature has not yet been reviewed. In this article most recent and relevant 12 studies, from different databases related to artificial inelegance techniques for wheeze detection has been selected for detailed review. It has been noticed that now trend is going to increase in this area, for personal assistance and continues monitoring of patient health. The literature reveals that 1) wheezes signals have enough information for the classification of patients according to disease severity level and type of disease, 2) significant work is required for identification of severity level of airway obstruction and pathology differentiation. Springer Fatimah, Ibrahim Jadeera, Phaik Geok Cheong Juliana , Usman Mohd Yazed, Ahmad Rizal, Razman Victor, S. Selvanayagam 2017 Book Section PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/17458/1/book%20chapter2.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/17458/7/5.%20Artificial%20Intelligence%20Techniques%20Used%20for%20Wheeze%20Sounds%20Analysis%20Review.pdf Nabi, F. G. and Sundaraj, K. and Kiang, L. C. and Palaniappan, R. and Sundaraj, S. and Ahamed, Nizam Uddin (2017) Artificial Intelligence Techniques Used for Wheeze Sounds Analysis: Review. In: 3rd International Conference on Movement, Health and Exercise: Engineering Olympic Success: From Theory to Practice. IFMBE Proceedings, 58 . Springer, Singapore, pp. 37-40. ISBN 978-981-10-3736-8 http://dx.doi.org/0.1007/978-981-10-3737-5_8 doi: 10.1007/978-981-10-3737-5_8
institution	Universiti Malaysia Pahang
building	UMP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang
content_source	UMP Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English English
topic	TS Manufactures
spellingShingle	TS Manufactures Nabi, F. G. Sundaraj, K. Kiang, L. C. Palaniappan, R. Sundaraj, S. Ahamed, Nizam Uddin Artificial Intelligence Techniques Used for Wheeze Sounds Analysis: Review
description	Wheezes are acoustic, adventitious, continues and high pitch pulmonary sounds produce due to airway obstruction, these sounds mostly exist in pneumonia and asthma patients. Artificial intelligence techniques have been extensively used for wheeze sound analysis to diagnose patient. The available literature has not yet been reviewed. In this article most recent and relevant 12 studies, from different databases related to artificial inelegance techniques for wheeze detection has been selected for detailed review. It has been noticed that now trend is going to increase in this area, for personal assistance and continues monitoring of patient health. The literature reveals that 1) wheezes signals have enough information for the classification of patients according to disease severity level and type of disease, 2) significant work is required for identification of severity level of airway obstruction and pathology differentiation.
author2	Fatimah, Ibrahim
author_facet	Fatimah, Ibrahim Nabi, F. G. Sundaraj, K. Kiang, L. C. Palaniappan, R. Sundaraj, S. Ahamed, Nizam Uddin
format	Book Section
author	Nabi, F. G. Sundaraj, K. Kiang, L. C. Palaniappan, R. Sundaraj, S. Ahamed, Nizam Uddin
author_sort	Nabi, F. G.
title	Artificial Intelligence Techniques Used for Wheeze Sounds Analysis: Review
title_short	Artificial Intelligence Techniques Used for Wheeze Sounds Analysis: Review
title_full	Artificial Intelligence Techniques Used for Wheeze Sounds Analysis: Review
title_fullStr	Artificial Intelligence Techniques Used for Wheeze Sounds Analysis: Review
title_full_unstemmed	Artificial Intelligence Techniques Used for Wheeze Sounds Analysis: Review
title_sort	artificial intelligence techniques used for wheeze sounds analysis: review
publisher	Springer
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/17458/1/book%20chapter2.pdf http://umpir.ump.edu.my/id/eprint/17458/7/5.%20Artificial%20Intelligence%20Techniques%20Used%20for%20Wheeze%20Sounds%20Analysis%20Review.pdf http://umpir.ump.edu.my/id/eprint/17458/ http://dx.doi.org/0.1007/978-981-10-3737-5_8
_version_	1643668187440480256
score	13.211869

Artificial Intelligence Techniques Used for Wheeze Sounds Analysis: Review

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