Gastric Electrical Impedance Tomography (gEIT) Based on a 3D Jacobian Matrix and Dual-Step Fuzzy Clustering Post-Processing

We propose a gastric electrical impedance tomography ( {g} EIT) scheme that uses a 3D full Jacobian matrix ^\ast \text{J} and dual-step fuzzy clustering post-processing to automatically extract the gastric content volume {V} . {g} EIT has two stages: (i) computation of the full 3D Jacobian matri...

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Published in:IEEE sensors journal 2022-07, Vol.22 (14), p.14336-14346
Main Authors: Darma, Panji Nursetia, Kawashima, Daisuke, Takei, Masahiro
Format: Article
Language:English
Subjects:
3D full Jacobian matrix
Abdomen
Clustering
Conductivity
Current injection
dual-step fuzzy clustering
Electrical impedance
Electrical impedance tomography
Electrodes
Errors
gastric imaging
gEIT
Impedance measurement
Jacobi matrix method
Jacobian matrices
Jacobian matrix
Sensors
Three-dimensional displays
Tomography
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description We propose a gastric electrical impedance tomography ( {g} EIT) scheme that uses a 3D full Jacobian matrix ^\ast \text{J} and dual-step fuzzy clustering post-processing to automatically extract the gastric content volume {V} . {g} EIT has two stages: (i) computation of the full 3D Jacobian matrix ^\ast \text{J} , including all combinations of the current injection pattern {I} and the impedance measurement {Z} , in order to reconstruct the volumetric abdomen conductivity \sigma ; and (ii) clustering of the gastric composition ^{k} \sigma (gastric wall {k}={1} , gastric content {k}={2} ) using dual-step fuzzy clustering post-processing, in order to automatically extract the volumetric gastric conductivity ^{k} \sigma . Our {g} EIT scheme is qualitatively evaluated using realistic abdomen phantoms representing three gastric accommodation phases, based on three different values for the gastric content volume filled with liquid meal: (A) {V}_{A} = {100} mL; (B) {V}_{B} = {300} mL; and (C) {V}_{C} = {600} mL, for two abdomen shapes (a normal abdomen {S} and a fatty abdomen {L} ) through both simulation and experimental studies. The results show that
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Our <inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT scheme is qualitatively evaluated using realistic abdomen phantoms representing three gastric accommodation phases, based on three different values for the gastric content volume filled with liquid meal: (A) <inline-formula> <tex-math notation="LaTeX">{V}_{A} = {100} </tex-math></inline-formula> mL; (B) <inline-formula> <tex-math notation="LaTeX">{V}_{B} = {300} </tex-math></inline-formula> mL; and (C) <inline-formula> <tex-math notation="LaTeX">{V}_{C} = {600} </tex-math></inline-formula> mL, for two abdomen shapes (a normal abdomen <inline-formula> <tex-math notation="LaTeX">{S} </tex-math></inline-formula> and a fatty abdomen <inline-formula> <tex-math notation="LaTeX">{L} </tex-math></inline-formula>) through both simulation and experimental studies. The results show that <inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT automatically estimates <inline-formula> <tex-math notation="LaTeX">{V} </tex-math></inline-formula> with a total mean volume error <inline-formula> <tex-math notation="LaTeX">\langle \widetilde {V}_{e}\rangle </tex-math></inline-formula> that is 65.14% lower than a standard adjacent method. At the same time, <inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT detects the gastric conductivity <inline-formula> <tex-math notation="LaTeX">\sigma </tex-math></inline-formula> more effectively, with a total mean conductivity error <inline-formula> <tex-math notation="LaTeX">\langle \widetilde {\textit {CE}} \rangle </tex-math></inline-formula> and total mean root mean square error <inline-formula> <tex-math notation="LaTeX">\langle \widetilde {\textit {rmse}}\rangle ~33.68 </tex-math></inline-formula>% and 25.53% lower than a standard adjacent method, respectively.]]></description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2022.3181052</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>3D full Jacobian matrix ; Abdomen ; Clustering ; Conductivity ; Current injection ; dual-step fuzzy clustering ; Electrical impedance ; Electrical impedance tomography ; Electrodes ; Errors ; gastric imaging ; gEIT ; Impedance measurement ; Jacobi matrix method ; Jacobian matrices ; Jacobian matrix ; Sensors ; Three-dimensional displays ; Tomography</subject><ispartof>IEEE sensors journal, 2022-07, Vol.22 (14), p.14336-14346</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-bbe2022b94e1f7540bc7a5cdbc67a9baa1bc6d3e6c14d372fde10a5d24075bde3</citedby><cites>FETCH-LOGICAL-c359t-bbe2022b94e1f7540bc7a5cdbc67a9baa1bc6d3e6c14d372fde10a5d24075bde3</cites><orcidid>0000-0001-9528-3100 ; 0000-0002-1523-6206 ; 0000-0003-3855-7202</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9794920$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,54794</link.rule.ids></links><search><creatorcontrib>Darma, Panji Nursetia</creatorcontrib><creatorcontrib>Kawashima, Daisuke</creatorcontrib><creatorcontrib>Takei, Masahiro</creatorcontrib><title>Gastric Electrical Impedance Tomography (gEIT) Based on a 3D Jacobian Matrix and Dual-Step Fuzzy Clustering Post-Processing</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description><![CDATA[We propose a gastric electrical impedance tomography (<inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT) scheme that uses a 3D full Jacobian matrix <inline-formula> <tex-math notation="LaTeX">^\ast \text{J} </tex-math></inline-formula> and dual-step fuzzy clustering post-processing to automatically extract the gastric content volume <inline-formula> <tex-math notation="LaTeX">{V} </tex-math></inline-formula>. <inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT has two stages: (i) computation of the full 3D Jacobian matrix <inline-formula> <tex-math notation="LaTeX">^\ast \text{J} </tex-math></inline-formula>, including all combinations of the current injection pattern <inline-formula> <tex-math notation="LaTeX">{I} </tex-math></inline-formula> and the impedance measurement <inline-formula> <tex-math notation="LaTeX">{Z} </tex-math></inline-formula>, in order to reconstruct the volumetric abdomen conductivity <inline-formula> <tex-math notation="LaTeX">\sigma </tex-math></inline-formula>; and (ii) clustering of the gastric composition <inline-formula> <tex-math notation="LaTeX">^{k} \sigma </tex-math></inline-formula> (gastric wall <inline-formula> <tex-math notation="LaTeX">{k}={1} </tex-math></inline-formula>, gastric content <inline-formula> <tex-math notation="LaTeX">{k}={2} </tex-math></inline-formula>) using dual-step fuzzy clustering post-processing, in order to automatically extract the volumetric gastric conductivity <inline-formula> <tex-math notation="LaTeX">^{k} \sigma </tex-math></inline-formula>. Our <inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT scheme is qualitatively evaluated using realistic abdomen phantoms representing three gastric accommodation phases, based on three different values for the gastric content volume filled with liquid meal: (A) <inline-formula> <tex-math notation="LaTeX">{V}_{A} = {100} </tex-math></inline-formula> mL; (B) <inline-formula> <tex-math notation="LaTeX">{V}_{B} = {300} </tex-math></inline-formula> mL; and (C) <inline-formula> <tex-math notation="LaTeX">{V}_{C} = {600} </tex-math></inline-formula> mL, for two abdomen shapes (a normal abdomen <inline-formula> <tex-math notation="LaTeX">{S} </tex-math></inline-formula> and a fatty abdomen <inline-formula> <tex-math notation="LaTeX">{L} </tex-math></inline-formula>) through both simulation and experimental studies. The results show that <inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT automatically estimates <inline-formula> <tex-math notation="LaTeX">{V} </tex-math></inline-formula> with a total mean volume error <inline-formula> <tex-math notation="LaTeX">\langle \widetilde {V}_{e}\rangle </tex-math></inline-formula> that is 65.14% lower than a standard adjacent method. At the same time, <inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT detects the gastric conductivity <inline-formula> <tex-math notation="LaTeX">\sigma </tex-math></inline-formula> more effectively, with a total mean conductivity error <inline-formula> <tex-math notation="LaTeX">\langle \widetilde {\textit {CE}} \rangle </tex-math></inline-formula> and total mean root mean square error <inline-formula> <tex-math notation="LaTeX">\langle \widetilde {\textit {rmse}}\rangle ~33.68 </tex-math></inline-formula>% and 25.53% lower than a standard adjacent method, respectively.]]></description><subject>3D full Jacobian matrix</subject><subject>Abdomen</subject><subject>Clustering</subject><subject>Conductivity</subject><subject>Current injection</subject><subject>dual-step fuzzy clustering</subject><subject>Electrical impedance</subject><subject>Electrical impedance tomography</subject><subject>Electrodes</subject><subject>Errors</subject><subject>gastric imaging</subject><subject>gEIT</subject><subject>Impedance measurement</subject><subject>Jacobi matrix method</subject><subject>Jacobian matrices</subject><subject>Jacobian matrix</subject><subject>Sensors</subject><subject>Three-dimensional displays</subject><subject>Tomography</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNo9kMtOwzAQRSMEElD4AMTGEhtYpNhxXMdL6Iui8pAoErtoYk9LqjQOdiJR-HkStWI1Z6R753GD4ILRPmNU3T6-jZ_7EY2iPmcJoyI6CE6YEEnIZJwcdsxpGHP5cRycer-mlCkp5EnwOwVfu1yTcYG6AyjIbFOhgVIjWdiNXTmoPrfkejWeLW7IPXg0xJYECB-RR9A2y6EkT9B6vwmUhowaKMK3GisyaX5-tmRYNL5Gl5cr8mp9Hb46q9H7tj8LjpZQeDzf117wPhkvhg_h_GU6G97NQ82FqsMsw-6xTMXIllLENNMShDaZHkhQGQBryXAcaBYbLqOlQUZBmCimUmQGeS-42s2tnP1q0Nfp2jaubFem0SBRCZWxEq2K7VTaWe8dLtPK5Rtw25TRtMs47TJOu1PSfcat53LnyRHxX6-kilVE-R9InXjh</recordid><startdate>20220715</startdate><enddate>20220715</enddate><creator>Darma, Panji Nursetia</creator><creator>Kawashima, Daisuke</creator><creator>Takei, Masahiro</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-9528-3100</orcidid><orcidid>https://orcid.org/0000-0002-1523-6206</orcidid><orcidid>https://orcid.org/0000-0003-3855-7202</orcidid></search><sort><creationdate>20220715</creationdate><title>Gastric Electrical Impedance Tomography (gEIT) Based on a 3D Jacobian Matrix and Dual-Step Fuzzy Clustering Post-Processing</title><author>Darma, Panji Nursetia ; Kawashima, Daisuke ; Takei, Masahiro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-bbe2022b94e1f7540bc7a5cdbc67a9baa1bc6d3e6c14d372fde10a5d24075bde3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>3D full Jacobian matrix</topic><topic>Abdomen</topic><topic>Clustering</topic><topic>Conductivity</topic><topic>Current injection</topic><topic>dual-step fuzzy clustering</topic><topic>Electrical impedance</topic><topic>Electrical impedance tomography</topic><topic>Electrodes</topic><topic>Errors</topic><topic>gastric imaging</topic><topic>gEIT</topic><topic>Impedance measurement</topic><topic>Jacobi matrix method</topic><topic>Jacobian matrices</topic><topic>Jacobian matrix</topic><topic>Sensors</topic><topic>Three-dimensional displays</topic><topic>Tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Darma, Panji Nursetia</creatorcontrib><creatorcontrib>Kawashima, Daisuke</creatorcontrib><creatorcontrib>Takei, Masahiro</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Darma, Panji Nursetia</au><au>Kawashima, Daisuke</au><au>Takei, Masahiro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gastric Electrical Impedance Tomography (gEIT) Based on a 3D Jacobian Matrix and Dual-Step Fuzzy Clustering Post-Processing</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><date>2022-07-15</date><risdate>2022</risdate><volume>22</volume><issue>14</issue><spage>14336</spage><epage>14346</epage><pages>14336-14346</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract><![CDATA[We propose a gastric electrical impedance tomography (<inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT) scheme that uses a 3D full Jacobian matrix <inline-formula> <tex-math notation="LaTeX">^\ast \text{J} </tex-math></inline-formula> and dual-step fuzzy clustering post-processing to automatically extract the gastric content volume <inline-formula> <tex-math notation="LaTeX">{V} </tex-math></inline-formula>. <inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT has two stages: (i) computation of the full 3D Jacobian matrix <inline-formula> <tex-math notation="LaTeX">^\ast \text{J} </tex-math></inline-formula>, including all combinations of the current injection pattern <inline-formula> <tex-math notation="LaTeX">{I} </tex-math></inline-formula> and the impedance measurement <inline-formula> <tex-math notation="LaTeX">{Z} </tex-math></inline-formula>, in order to reconstruct the volumetric abdomen conductivity <inline-formula> <tex-math notation="LaTeX">\sigma </tex-math></inline-formula>; and (ii) clustering of the gastric composition <inline-formula> <tex-math notation="LaTeX">^{k} \sigma </tex-math></inline-formula> (gastric wall <inline-formula> <tex-math notation="LaTeX">{k}={1} </tex-math></inline-formula>, gastric content <inline-formula> <tex-math notation="LaTeX">{k}={2} </tex-math></inline-formula>) using dual-step fuzzy clustering post-processing, in order to automatically extract the volumetric gastric conductivity <inline-formula> <tex-math notation="LaTeX">^{k} \sigma </tex-math></inline-formula>. Our <inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT scheme is qualitatively evaluated using realistic abdomen phantoms representing three gastric accommodation phases, based on three different values for the gastric content volume filled with liquid meal: (A) <inline-formula> <tex-math notation="LaTeX">{V}_{A} = {100} </tex-math></inline-formula> mL; (B) <inline-formula> <tex-math notation="LaTeX">{V}_{B} = {300} </tex-math></inline-formula> mL; and (C) <inline-formula> <tex-math notation="LaTeX">{V}_{C} = {600} </tex-math></inline-formula> mL, for two abdomen shapes (a normal abdomen <inline-formula> <tex-math notation="LaTeX">{S} </tex-math></inline-formula> and a fatty abdomen <inline-formula> <tex-math notation="LaTeX">{L} </tex-math></inline-formula>) through both simulation and experimental studies. The results show that <inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT automatically estimates <inline-formula> <tex-math notation="LaTeX">{V} </tex-math></inline-formula> with a total mean volume error <inline-formula> <tex-math notation="LaTeX">\langle \widetilde {V}_{e}\rangle </tex-math></inline-formula> that is 65.14% lower than a standard adjacent method. At the same time, <inline-formula> <tex-math notation="LaTeX">{g} </tex-math></inline-formula>EIT detects the gastric conductivity <inline-formula> <tex-math notation="LaTeX">\sigma </tex-math></inline-formula> more effectively, with a total mean conductivity error <inline-formula> <tex-math notation="LaTeX">\langle \widetilde {\textit {CE}} \rangle </tex-math></inline-formula> and total mean root mean square error <inline-formula> <tex-math notation="LaTeX">\langle \widetilde {\textit {rmse}}\rangle ~33.68 </tex-math></inline-formula>% and 25.53% lower than a standard adjacent method, respectively.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSEN.2022.3181052</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-9528-3100</orcidid><orcidid>https://orcid.org/0000-0002-1523-6206</orcidid><orcidid>https://orcid.org/0000-0003-3855-7202</orcidid></addata></record>
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subjects 3D full Jacobian matrix
Abdomen
Clustering
Conductivity
Current injection
dual-step fuzzy clustering
Electrical impedance
Electrical impedance tomography
Electrodes
Errors
gastric imaging
gEIT
Impedance measurement
Jacobi matrix method
Jacobian matrices
Jacobian matrix
Sensors
Three-dimensional displays
Tomography
title Gastric Electrical Impedance Tomography (gEIT) Based on a 3D Jacobian Matrix and Dual-Step Fuzzy Clustering Post-Processing
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