Time series of EIT measurements and images during lung ventilation based on principal component analysis

Abstract

The aim of this paper is to propose a useful method for exploring regional ventilation and perfusion in the chest and also separation of pulmonary and cardiac changes. The approach is based on estimating both electrical impedance tomography (EIT) measurements and reconstructed images by means of principal component analysis (PCA). In the experiments in vivo, 43 cycles of heart-beat rhythm could be detected by PCA when the volunteer held breath; 9 breathing cycles and 50 heart-beat cycles could be detected by PCA when the volunteer breathed normally. The results indicate that the rhythms of cardiac activity and respiratory process can be exploited and separated through analyzing the boundary measurements by PCA without image reconstruction.

Keywords

electrical impedance tomography / image reconstruction / principal component analysis

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