Integrated Strategy to Mitigate Motion-Induced Artifacts During Seizures in Electrical Impedance Tomography

Accurate identification of the epileptogenic zone (EZ) is essential for epilepsy patients to achieve successful surgical outcomes. Electrical impedance tomography (EIT) has the potential to enhance the precision of EZ localization. However, motion-induced image artifacts present a considerable chall...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 25; no. 9; pp. 15155 - 15166
Main Authors: Xu, Jiaming, Yang, Jingrong, Wu, Xinyu, Dong, Xiuzhen, Shi, Xuetao, Yang, Fang, Wang, Lei
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Back propagation networks
Biomedical imaging
Correlation coefficients
Electric potential
Electrical impedance
Electrical impedance tomography
Electrical impedance tomography (EIT)
electrode disconnection
electrode position uncertainty
Electrodes
Epilepsy
Interference
Localization
Location awareness
Medical imaging
motion-induced artifacts
Neural networks
original boundary voltage
Particle swarm optimization
Surgery
Tomography
Training
Uncertainty
Voltage
Voltage measurement
Whale optimization algorithms
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:Accurate identification of the epileptogenic zone (EZ) is essential for epilepsy patients to achieve successful surgical outcomes. Electrical impedance tomography (EIT) has the potential to enhance the precision of EZ localization. However, motion-induced image artifacts present a considerable challenge to accurate EIT imaging. This study aims to mitigate motion-induced artifacts in EIT. We propose an integrated strategy to address the artifacts stemming from two scenarios associated with the motion: electrode disconnection and position uncertainty. We employ Z -score and Pearson's correlation coefficient (PCC) of original boundary voltage to identify the disconnected electrodes, a whale optimization algorithm (WOA) optimized backpropagation neural network (BPNN) to correct erroneous data induced by disconnected electrodes and particle swarm optimized variational mode decomposition (PSO-VMD) to suppress the motion interference induced by position uncertainty electrodes. The results show that both EIT voltage and original boundary voltage return to their normal level after motion-induced artifacts suppression, resulting in a marked enhancement in EIT imaging quality. The strategy's efficacy is confirmed through both animal experiments and trials with healthy volunteers. The methodology presented in this article is adept at mitigating motion interference in brain EIT, improving the accuracy and reliability of EZ localization. It also helps to expand the clinical application scenarios of EIT, enabling the monitoring of other diseases that might produce motion-related interference.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2025.3549166