Electrical impedance tomography (EIT) is a promising medical technique for monitoring brain injury through the reflection of electrical impedance changes in internal brain tissues. However, the contact impedances at the electrode-scalp interfaces can still notably affect the EIT accuracy. Hence, we propose a method for real-time monitoring the multi-channel contact impedances of cerebral EIT using a reference electrode attached close to the head vertex. This method allows to approximate inter-channel differences and changes over time of multi-channel contact impedances. The experimental results on 36 volunteers show that the maximum relative inter-channel difference obtained from 16 disposable surface electrodes reaches 18.7%, and the maximum relative change over time reaches 19.2%. The proposed method can assist clinicians in real-time monitoring of contact conditions from the multiple electrode-scalp interfaces during the clinical application of the cerebral EIT.

Intracranial hypertension can potentially cause ischemia, herniation, and even death. Mannitol dehydration treatment is a common method for reducing increased intracranial pressure (ICP). However, the efficacy varies among patients and improper use of mannitol may cause severe side effects. This study aims to find ways to individually evaluate and provide guidance for mannitol dehydration treatments based on dynamic brain electrical impedance tomography (EIT). First, clustering methods were applied to analyze the relationship between EIT data and patients’ different responses to mannitol. With the clustering results as well as empirical experiences, doctors could establish a rule for classifying the efficacy of mannitol dehydration treatments. Then, to rapidly categorize following EIT datasets, an automatic classifier was developed using the multiclass support vector machines (SVMs). Additionally, to enhance the accuracy of the classifier, a novel priori and principle components (PPCs) based index was introduced as the input data for training the classifier. The experimental results demonstrated that different clustering methods yielded similar results and the optimal number of clusters was three, which corresponded to three types of efficacy of mannitol. For the automatic classifier, its classification accuracy improved to 93.3% with the PPCs index. As such, it can be concluded that brain EIT promises to be an effective tool for individually evaluating the efficacy of the dehydration treatments and make guidance for rational use of mannitol.

Head 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. This 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. To 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. In 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.