Academic Journal
Cardiac and respiratory signal separation in electrical impedance tomography (EIT).
| Τίτλος: | Cardiac and respiratory signal separation in electrical impedance tomography (EIT). |
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| Συγγραφείς: | Hidayah MN; Department Physics, Universitas Sebelas Maret (UNS), Surakarta 57126, Indonesia., Baidillah MR; Research Center for Electronics, National Research and Innovation Agency (BRIN), KST Samaun Samadikun, Bandung 40135, Indonesia., Suharyana S; Department Physics, Universitas Sebelas Maret (UNS), Surakarta 57126, Indonesia. |
| Πηγή: | Biomedical physics & engineering express [Biomed Phys Eng Express] 2026 Sep 17; Vol. 12 (5). Date of Electronic Publication: 2026 Sep 17. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: IOP Publishing Ltd Country of Publication: England NLM ID: 101675002 Publication Model: Electronic Cited Medium: Internet ISSN: 2057-1976 (Electronic) Linking ISSN: 20571976 NLM ISO Abbreviation: Biomed Phys Eng Express Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol : IOP Publishing Ltd., [2015]- |
| Ιατρικοί όροι (MeSH): | Tomography*/methods , Image Processing, Computer-Assisted*/methods , Heart*/physiology , Heart*/diagnostic imaging , Electric Impedance* , Respiration*, Humans ; Principal Component Analysis ; Algorithms ; Computer Simulation ; Wavelet Analysis ; Signal Processing, Computer-Assisted |
| Περίληψη: | Electrical impedance tomography (EIT) is a promising non-invasive imaging modality for respiratory and cardiac monitoring. However, separating cardiac signals from the dominant respiratory component remains challenging because both physiological processes are simultaneously embedded in the measured impedance signals. This study investigates the influence of EIT measurement patterns on cardiac-respiratory signal separation using four methods: continuous wavelet transform, empirical mode decomposition, independent component analysis, and principal component analysis (PCA). Numerical simulations were performed in EIDORS using a realistic thoracic geometry reconstructed from the POPI dataset with a GREIT-based image reconstruction framework. Four measurement configurations were evaluated: adjacent-adjacent, adjacent-Skip 4, Skip 4-adjacent, and Skip 4-Skip 4. Signal separation performance was assessed using the Cross Correlation (CC) between the extracted and reference signals, while the normalized root mean square error was additionally used to evaluate the reconstructed cardiac waveform. Among all evaluated methods, PCA consistently achieved the highest waveform similarity, reaching a Lung CC of 98.12% and a Heart CC of 99.56% under the Skip 4-Skip 4 measurement pattern. Visual inspection of the reconstructed images further demonstrated clearer separation of respiratory and cardiac conductivity distributions using this configuration. These results indicate that combining PCA with a wide electrode measurement pattern improves cardiac-respiratory signal separation in simulated EIT measurements. (© 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.) |
| Contributed Indexing: | Keywords: Cardiac impedance; electrical impedance tomography (EIT); measurement pattern; physiological signal separation; principal component analysis; pulmonary impedance |
| Entry Date(s): | Date Created: 20260908 Date Completed: 20260917 Latest Revision: 20260924 |
| Update Code: | 20260925 |
| DOI: | 10.1088/2057-1976/aea425 |
| PMID: | 42710529 |
| Βάση Δεδομένων: | MEDLINE |
| FullText | Text: Availability: 0 |
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| Header | DbId: cmedm DbLabel: MEDLINE An: 42710529 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Cardiac and respiratory signal separation in electrical impedance tomography (EIT). – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Hidayah+MN%22">Hidayah MN</searchLink>; Department Physics, Universitas Sebelas Maret (UNS), Surakarta 57126, Indonesia.<br /><searchLink fieldCode="AU" term="%22Baidillah+MR%22">Baidillah MR</searchLink>; Research Center for Electronics, National Research and Innovation Agency (BRIN), KST Samaun Samadikun, Bandung 40135, Indonesia.<br /><searchLink fieldCode="AU" term="%22Suharyana+S%22">Suharyana S</searchLink>; Department Physics, Universitas Sebelas Maret (UNS), Surakarta 57126, Indonesia. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101675002%22">Biomedical physics & engineering express</searchLink> [Biomed Phys Eng Express] 2026 Sep 17; Vol. 12 (5). <i>Date of Electronic Publication: </i>2026 Sep 17. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22IOP+Publishing+Ltd%22">IOP Publishing Ltd </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101675002 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2057-1976 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220571976%22">20571976 </searchLink><i>NLM ISO Abbreviation: </i>Biomed Phys Eng Express <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Original Publication</i>: Bristol : IOP Publishing Ltd., [2015]- – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Tomography%22">Tomography*</searchLink>/<searchLink fieldCode="MM" term="%22Tomography+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted%22">Image Processing, Computer-Assisted*</searchLink>/<searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Heart%22">Heart*</searchLink>/<searchLink fieldCode="MM" term="%22Heart+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Heart%22">Heart*</searchLink>/<searchLink fieldCode="MM" term="%22Heart+diagnostic+imaging%22">diagnostic imaging</searchLink> <br /><searchLink fieldCode="MM" term="%22Electric+Impedance%22">Electric Impedance*</searchLink> <br /><searchLink fieldCode="MM" term="%22Respiration%22">Respiration*</searchLink><br /><searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Principal+Component+Analysis%22">Principal Component Analysis</searchLink> ; <searchLink fieldCode="MH" term="%22Algorithms%22">Algorithms</searchLink> ; <searchLink fieldCode="MH" term="%22Computer+Simulation%22">Computer Simulation</searchLink> ; <searchLink fieldCode="MH" term="%22Wavelet+Analysis%22">Wavelet Analysis</searchLink> ; <searchLink fieldCode="MH" term="%22Signal+Processing%2C+Computer-Assisted%22">Signal Processing, Computer-Assisted</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Electrical impedance tomography (EIT) is a promising non-invasive imaging modality for respiratory and cardiac monitoring. However, separating cardiac signals from the dominant respiratory component remains challenging because both physiological processes are simultaneously embedded in the measured impedance signals. This study investigates the influence of EIT measurement patterns on cardiac-respiratory signal separation using four methods: continuous wavelet transform, empirical mode decomposition, independent component analysis, and principal component analysis (PCA). Numerical simulations were performed in EIDORS using a realistic thoracic geometry reconstructed from the POPI dataset with a GREIT-based image reconstruction framework. Four measurement configurations were evaluated: adjacent-adjacent, adjacent-Skip 4, Skip 4-adjacent, and Skip 4-Skip 4. Signal separation performance was assessed using the Cross Correlation (CC) between the extracted and reference signals, while the normalized root mean square error was additionally used to evaluate the reconstructed cardiac waveform. Among all evaluated methods, PCA consistently achieved the highest waveform similarity, reaching a Lung CC of 98.12% and a Heart CC of 99.56% under the Skip 4-Skip 4 measurement pattern. Visual inspection of the reconstructed images further demonstrated clearer separation of respiratory and cardiac conductivity distributions using this configuration. These results indicate that combining PCA with a wide electrode measurement pattern improves cardiac-respiratory signal separation in simulated EIT measurements.<br /> (© 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.) – Name: SubjectMinor Label: Contributed Indexing Group: Data: <i>Keywords: </i>Cardiac impedance; electrical impedance tomography (EIT); measurement pattern; physiological signal separation; principal component analysis; pulmonary impedance – Name: DateEntry Label: Entry Date(s) Group: Date Data: <i>Date Created: </i>20260908 <i>Date Completed: </i>20260917 <i>Latest Revision: </i>20260924 – Name: DateUpdate Label: Update Code Group: Date Data: 20260925 – Name: DOI Label: DOI Group: ID Data: 10.1088/2057-1976/aea425 – Name: AN Label: PMID Group: ID Data: 42710529 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=42710529 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1088/2057-1976/aea425 Languages: – Code: eng Text: English Subjects: – SubjectFull: Humans Type: general – SubjectFull: Principal Component Analysis Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Computer Simulation Type: general – SubjectFull: Wavelet Analysis Type: general – SubjectFull: Signal Processing, Computer-Assisted Type: general – SubjectFull: Tomography methods Type: general – SubjectFull: Image Processing, Computer-Assisted methods Type: general – SubjectFull: Heart physiology Type: general – SubjectFull: Heart diagnostic imaging Type: general – SubjectFull: Electric Impedance Type: general – SubjectFull: Respiration Type: general Titles: – TitleFull: Cardiac and respiratory signal separation in electrical impedance tomography (EIT). Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hidayah MN – PersonEntity: Name: NameFull: Baidillah MR – PersonEntity: Name: NameFull: Suharyana S IsPartOfRelationships: – BibEntity: Dates: – D: 17 M: 09 Text: 2026 Sep 17 Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 2057-1976 Numbering: – Type: volume Value: 12 – Type: issue Value: 5 Titles: – TitleFull: Biomedical physics & engineering express Type: main |
| ResultId | 1 |