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 |
| ISSN: | 2057-1976 |
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| DOI: | 10.1088/2057-1976/aea425 |