Publication:
Comparison of burst parameters of IMSNA during handgrip exercise calculated by computer automated detection program with investigator manually calculated

dc.contributor.authorWarakorn Charoensuken_US
dc.contributor.authorRichard G. Shiavien_US
dc.contributor.authorFernando B. Costaen_US
dc.contributor.otherMahidol Universityen_US
dc.contributor.otherVanderbilt Universityen_US
dc.date.accessioned2018-07-24T03:41:14Z
dc.date.available2018-07-24T03:41:14Z
dc.date.issued2004-12-01en_US
dc.description.abstractMicroneurography is a technique in which electrodes are inserted percutaneously into peripheral nerves in humans for recording of single or multi-unit action potentials. The integrated muscle sympathetic nerve activity (IMSNA) are used to study the neural control of autonomie nervous system. In this research, we developed the automated IMSNA detection program and applied the program to detect burst parameters of IMSNA to seven normal subjects during the handgrip exercise experiments. And the results were compared with the result from the experienced investigator, which is manually calculated. The result of comparison was found to have a very high correlation (0.9821) between the developed automated detection program and the investigator. ©2004IEEE.en_US
dc.identifier.citationIEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol.C, (2004)en_US
dc.identifier.other2-s2.0-27944495715en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/21314
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=27944495715&origin=inwarden_US
dc.subjectEngineeringen_US
dc.titleComparison of burst parameters of IMSNA during handgrip exercise calculated by computer automated detection program with investigator manually calculateden_US
dc.typeConference Paperen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=27944495715&origin=inwarden_US

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