Publication:
Monitoring Respiration Activity with Bulk Conductivity in a 16-electrode Configuration

dc.contributor.authorBuathong P.
dc.contributor.authorPhisaiphan A.
dc.contributor.authorLaoriam P.
dc.contributor.authorAramphianlert W.
dc.contributor.correspondenceBuathong P.
dc.contributor.otherSrinakharinwirot University
dc.date.accessioned2025-05-28T07:56:42Z
dc.date.issued2024-01-01
dc.date.issuedBE2567-01-01
dc.description.abstractRespiration monitoring is a method for diagnosing the health of the lungs. Current imaging such as CT and MRI still have a limitation of low temporal resolution that could be difficult to be used as monitoring devices. Electrical Impedance Tomography (EIT) is an imaging technique with good temporal resolution, but it still requires substantial reconstruction time while the artifacts are usually observable in the images. In this study, bulk conductivity was used to monitor the respiration activity, instead of monitoring based on the reconstruction images. The boundary voltage information that was for EIT image reconstruction was now used for the bulk conductivity estimation. Experimental results on a subject's chest found that the bulk conductivity can efficiently represent the respiration activity. It was consistent with the change that appeared in the reconstruction images and had a very high correlation with the voltage measurement with a -0.98 correlation coefficient.
dc.identifier.citationProceeding - 12th International Electrical Engineering Congress: Smart Factory and Intelligent Technology for Tomorrow, iEECON 2024 (2024)
dc.identifier.doi10.1109/iEECON60677.2024.10537874
dc.identifier.scopus2-s2.0-85195785543
dc.identifier.urihttps://hdl.handle.net/20.500.14740/20929
dc.rights.holderSCOPUS
dc.subjectEngineering
dc.subjectComputer Science
dc.subjectMathematics
dc.titleMonitoring Respiration Activity with Bulk Conductivity in a 16-electrode Configuration
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.titleProceeding - 12th International Electrical Engineering Congress: Smart Factory and Intelligent Technology for Tomorrow, iEECON 2024
oairecerif.author.affiliationSrinakharinwirot University
swu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195785543&origin=inward

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