Publication:
Human breathing classification using electromyography signal with features based on mel-frequency cepstral coefficients

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cris.author.scopus-author-id 57210551450
cris.author.scopus-author-id 26029734700
cris.author.scopus-author-id 24403085300
cris.author.scopus-author-id 36901996000
cris.author.scopus-author-id 36988141300
cris.virtual.department Universiti Malaysia Perlis
cris.virtual.department Universiti Malaysia Perlis
cris.virtualsource.department 0e392012-3c29-42d3-8198-edd5e2f96381
cris.virtualsource.department 90ddadf3-abd4-4249-b6ad-91a5cd2b0496
dc.contributor.author Ahmad Nasrul Norali
dc.contributor.author Abdullah A.H.
dc.contributor.author Zulkifli Zakaria
dc.contributor.author Norasmadi Abdul Rahim
dc.contributor.author Nataraj S.K.
dc.date.accessioned 2025-01-13T06:34:52Z
dc.date.available 2025-01-13T06:34:52Z
dc.date.issued 2017-01-01
dc.description.abstract Typical method on assessing the human breathing characteristics is based on measurements of breathing air parameters. Another possible method for human breathing assessment is through the analysis of respiratory muscles electromyography (EMG) signal. The EMG signal from different breathing task will be analyzed in order to determine the characteristics of the EMG signal pattern. Thus, feature extraction need to be done on the EMG signals. This paper will look into the use of Mel-Frequency Cepstral Coefficients (MFCC) in providing the features for EMG signal. Analysis is done using different data analysis frame sizes. EMG signal classification is done using K-Nearest Neighbour. Results shows that MFCC is a good feature extraction method for this purpose with classification accuracy exceeds more than 90% for data analysis frame size of 2000 ms, 4000 ms, 5000 ms and 10000 ms.
dc.identifier.scopus 2-s2.0-85041572280
dc.identifier.uri https://hdl.handle.net/20.500.14170/11328
dc.language.iso en
dc.relation.grantno undefined
dc.relation.ispartof International Journal of Integrated Engineering
dc.relation.ispartofseries International Journal of Integrated Engineering
dc.relation.issn 2229838X
dc.subject Electromyography
dc.subject Human breathing
dc.subject Respiratory muscles
dc.subject MFCC
dc.title Human breathing classification using electromyography signal with features based on mel-frequency cepstral coefficients
dc.type Journal
dspace.entity.type Publication
oaire.citation.endPage 92
oaire.citation.issue 4
oaire.citation.startPage 85
oaire.citation.volume 9
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation International Islamic University Malaysia
oairecerif.author.affiliation Universiti Malaysia Perlis
oairecerif.author.affiliation Universiti Malaysia Perlis
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.scopus-author-id 57210551450
person.identifier.scopus-author-id 26029734700
person.identifier.scopus-author-id 24403085300
person.identifier.scopus-author-id 36901996000
person.identifier.scopus-author-id 36988141300
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