Scalp Voltage Response to Conductivity Changes in the Brain in the Application of Electrical Impedance Tomography (EIT)

Suksawang, Sukritta; Niamsri, Kamonwan; Ouypornkochagorn, Taweechai

doi:10.1109/ecticon.2018.8620023

Cited by 2 publications

(1 citation statement)

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“…Tang et al studied the correlation between skull structure and conductivity changes, and believed that the uncertainty of skull conductivity and thickness should be incorporated into the head model during the modeling process [10]. Suksawang et al investigated the effect of uncertainty in excitation frequency and activation area volume on scalp voltage response [11]. The uncertainty influence on EIT results has attracted the attention of researchers in the world.…”

Section: Introductionmentioning

confidence: 99%

High-Dimensional Uncertainty Quantification in Electrical Impedance Tomography Forward Problem Based on Deep Neural Network

Yun

Wang

et al. 2023

IEEE Access

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In electrical impedance tomography (EIT), the uncertainty of conductivity distribution may cause the uncertainty in the forward calculation and further affect the inverse problem. In this paper, an improved univariate dimension reduction method based on deep neural network (DNN-UDR) is proposed for the high-dimensional uncertainty quantification in EIT forward problem. Firstly, DNN is studied to build a substitute model for EIT forward problem in order to solve the high-dimensional problem. Three normalized circular finite element models are established with random uniform conductivity distribution. Then UDR is used to analyze and quantify the uncertainty in the simulation with the form of probability. Compared with Monte Carlo simulation (MCS), the probability distribution of voltage is fitted, and the quantification indicators such as mean, variance, variation coefficient and covariance, are also consistent. On the other hand, with the increase of parameter dimensions, DNN-UDR accelerates the computations obviously. This indicates that DNN-UDR is effective and has high structural stability, accurate prediction results and high computational efficiency.

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Section: Introductionmentioning

confidence: 99%