About: Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #83024 Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds
sameAs	Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds
http://eprints.org/ontology/hasDocument	Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds (PDF) Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds (Other) Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds (Other) Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds (Other) Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds (Other) Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds (Other)
http://eprints.org/ontology/hasPublished	Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds (PDF)
dc:hasVersion	Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds (PDF)
Title	Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2020-08-27
Creator	L. Li Liqun Wu
status	peer reviewed published
Publisher	IEEE
abstract	Respiratory condition has received a great amount of attention nowadays since respiratory diseases recently become the globally leading causes of death. Traditionally, stethoscope is applied in early diagnosis but it requires clinician with extensive training experience to provide accurate diagnosis. Accordingly, a subjective and fast diagnosing solution of respiratory diseases is highly demanded. Adventitious respiratory sounds (ARSs), such as crackle, are mainly concerned during diagnosis since they are indication of various respiratory diseases. Therefore, the characteristics of crackle are informative and valuable regarding to develop a computerised approach for pathology-based diagnosis. In this work, we propose a framework combining random forest classifier and Empirical Mode Decomposition (EMD) method focusing on a multi-classification task of identifying subjects in 6 respiratory conditions (healthy, bronchiectasis, bronchiolitis, COPD, pneumonia and URTI). Specifically, 14 combinations of respiratory sound segments were compared and we found segmentation plays an important role in classifying different respiratory conditions. The classifier with best performance (accuracy = 0.88, precision = 0.91, recall = 0.87, specificity = 0.91, F1-score = 0.81) was trained with features extracted from the combination of early inspiratory phase and entire inspiratory phase. To our best knowledge, we are the first to address the challenging multi-classification problem.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	Q Science
list of authors	https://kar.kent.ac.uk/id/eprint/83024#authors
presented at	2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #83024 Investigating into segmentation methods for diagnosis of respiratory diseases using adventitious respiratory sounds

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