An on-line processing strategy for head movement interferences removal of dynamic brain electrical impedance tomography based on wavelet decomposition

Abstract

Background 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. Methods 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. Results 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. Conclusion 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.

Advances in electrical impedance tomography-based brain imaging

“…The results showed that SPR could improve brain EIT image quality and recover intracranial perturbations from certain incorrect measurements [129]. In addition, the FMMU group proposed an online strategy to manage head movement interference in brain EIT data based on the distribution characteristics of wavelet coefficients [130]; the strategy reduced movement interference in the data and improved the quality of the reconstructed images.…”

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

“…Several methods have been proposed for ECG enhancement such as independent component analysis (ICA) [187], advanced averaging [188], [189], adaptive filtering [190], SVD [191], maximally decimated perfectreconstruction FIR filter banks [192], wavelet transform [193], [194], and nonlinear filter banks [195]. Generally, one of the foremost challenges in the ECG-based biometric system is the separation of the desired signal from several types of noise such as baseline wander, power line interference, motion artifacts, muscle noise, and other interference [196], [197].…”

“…(a) Baseline wander: Baseline wander is a slow-varying artifact [196], which essentially results from the skinelectrode impedance variation that emerges in the form of a low-frequency noise merged with the ECG signal [198]. Impedance variation can manifest as a result of the individual breath, the electrode-skin contact, and smooth movements [197]. Moreover, baseline wander is a typical artifact that corrupts the recorded ECG signals and stems from respiration at frequency wandering within 0.15−0.3 Hz, which can be filtered using a standard high pass digital filter [199].…”

Real‐Time Detection of Hemothorax and Monitoring its Progression in a Piglet Model by Electrical Impedance Tomography: A Feasibility Study

“…Furthermore, for the case of complete electrode disconnection, Zhang et al recently conceived a weighted correlation coefficient method to test multiple problematic electrodes and employed data from grey model predictions for compensatory processing [58]. Subsequently, for partial electrode disconnection, Zhang et al proposed an online strategy based on wavelet decomposition to manage the EIT data from partially connected electrodes [59]. Conse-quently, in the future, approaches including special image algorithm and data preprocessing method to reduce the influence of body movement interferences are suggested to be used in clinical EIT application for delayed hemothorax detection.…”