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

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 ventilation/ perfusion (V/Q) ratio. Previously, various methods have been used to extract these components; however, these have not been able to effectively separate and validate cardiac- and pulmonary- related images.

This study aims at validating and developing a novel method to separate cardiac- and pulmonary- related components based on the EIT simulation field of view and to simultaneously reconstruct the individual images instantly.

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 introducing the empirical mode decomposition (EMD). The PCA template functions for cardiacrelated imaging and intrinsic mode functions (IMFs) of EMD for lung-related imaging are then adapted to input signals.

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, respectively. 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.

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.