Abstract
BackgroundHead movement interferences are a common problem during prolonged dynamic brain electrical impedance tomography (EIT) clinical monitoring. Head movement interferences mainly originate from body movements of patients and nursing procedures performed by medical staff, etc. These body movements will lead to variation in boundary voltage signals, which affects image reconstruction.MethodsThis study employed a data preprocessing method based on wavelet decomposition to inhibit head movement interferences in brain EIT data. Mixed Gaussian models were applied to describe the distribution characteristics of brain EIT data. We identified head movement signal through the differences in distribution characteristics of corresponding wavelet decomposition coefficients between head movement artifacts and normal signals, and then managed the contaminated data with improved on-line wavelet processing methods.ResultsTo validate the efficacy of the method, simulated signal experiments and human data experiments were performed. In the simulation experiment, the simulated movement artifact was significantly reduced and data quality was improved with indicators’ increase in PRD and correlation coefficient. Human data experiments demonstrated that this method effectively suppressed head movement in signals and reduce artifacts resulting from head movement artifacts in images.ConclusionIn this paper, we proposed an on-line strategy to manage the head movement interferences from the brain EIT data based on the distribution characteristics of wavelet coefficients. Our strategy is capable of reducing the movement interference in the data and improving the reconstructed images. This work would improve the clinical practicability of brain EIT and contribute to its further promotion.
Highlights
Head movement interferences are a common problem during prolonged dynamic brain electrical impedance tomography (EIT) clinical monitoring
The change of connection status is a common occurrence during our previous clinical studies because of factors like: patient’s body movements and nursing procedures performed by medical staff
Influences of head movement interferences on boundary voltage The EIT image reconstruction process can be briefly summarized as: y = Bx where x represents input data, which is a frame of boundary voltage data, y represents the conductivity distribution of the target field, and B is the construction matrix which is determined by the finite element model (FEM) of the target field, and is closely related to the number of model elements, number of boundary measurement values, background conductivity, electrode model, etc
Summary
Head movement interferences are a common problem during prolonged dynamic brain electrical impedance tomography (EIT) clinical monitoring. The EIT system uses 16 electrodes placed uniformly on the head to apply safe currents and measures boundary voltage at two different instants, reconstructs intracranial impedance changes between the two instants according to Acerta in algorithm [1, 2]. The change of connection status is a common occurrence during our previous clinical studies because of factors like: patient’s body movements (head rotation, body turning) and nursing procedures performed by medical staff. These body movements lead to head movements and change the electrode–skin contact status, which introduce movement interferences in data collection and image reconstruction [3, 4]. There is an urgent need for appropriate methods to process the movement interferences if we want to further promote the dynamic brain EIT research
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