Separation of Heart and Lung-related Signals in Electrical Impedance Tomography Using Empirical Mode Decomposition

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Abstract

Background: Electrical impedance tomography (EIT) can be used for continuous monitoring of pulmonary ventilation. However, no proper method has been developed for the separation of pulmonary ventilation and perfusion signals and the measurement of the associated venti-lation/perfusion (V/Q) ratio. Previously, various methods have been used to extract these compo-nents; however, these have not been able to effectively separate and validate cardiac-and pulmo-nary-related images. Aims: This study aims at validating and developing a novel method to separate cardiac-and pul-monary-related components based on the EIT simulation field of view and to simultaneously re-construct the individual images instantly. Methods: Our approach combines the advantages of the principal component analysis (PCA) and processes that originally measure EIT data instead of handling a series of EIT images, thus intro-ducing the empirical mode decomposition (EMD). The PCA template functions for cardiac-related imaging and intrinsic mode functions (IMFs) of EMD for lung-related imaging are then adapted to input signals. Results: The proposed method enables the separation of cardiac-and lung-related components by adjusting the proportion of the key components related to lung imaging, which are the fourth component (PC4) and the first component (IMF1) in PCA-and EMD-based methods, respective-ly. The preliminary results on the application of the method to real human EIT data revealed the consistently better performance and optimal computation compared with previous methods. Conclusion: This study proposes a novel method for applying EIT to evaluate the best time of V/Q matching on the cardiovascular and respiratory systems; this aspect can be investigated in future research.

Original languageEnglish
Pages (from-to)1396-1415
Number of pages20
JournalCurrent Medical Imaging
Volume18
Issue number13
DOIs
Publication statusPublished - 2022 Nov

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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