A fast ERT system modification based on compressed sensing algorithm

Zhang, Wei; Tan, Chao; Dong, Feng

doi:10.1109/i2mtc.2018.8409586

Cited by 1 publication

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, EIT requires a temporal division measurement to switch the excitation conditions, in order to obtain sufficient data for solving an ill-posed inverse problem [23]. Compressed sensing (CS) is an efficient method to reduce the number of electrodes and the excitation conditions and useful for high speed and low power consuming measurement [24]- [26]. However, it is difficult to drastically reduce the excitation conditions without loosing the high spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Efficient and Robust Detection of Local Impedance Changes Using Selected Electrical Excitation Conditions

et al. 2020

View full text Add to dashboard Cite

Bioelectrical impedance method is useful for evaluating tissue state such as muscle injury but there is a trade off between efficiency and robustness in detection of various local impedance changes. To solve this problem, we proposed a method using compressed electrical impedance sensing and discrimination analysis. We employed multiple excitation conditions like electrical impedance tomography, and investigated the effective excitation conditions. We then used both simulation and measurement data for efficient training process. In our experiment, we implemented a simulator and a measurement system with 16 electrodes and biological phantoms, and evaluated the discrimination rate for various impedance distribution by assuming muscle injury. As a result, we found that 6/120 excitation conditions are sufficient for detection of local impedance changes in a simple model and sufficient blending ratio of the measurement data to simulation data was 10.6% to perform 90% discrimination. The experimental result also indicated that the proposed method was robust for injury size, injury position, and body shape and 90% discrimination was achieved using 13/120 excitation conditions for complex models including skin, bone, and muscle area.

show abstract

Section: Introductionmentioning

confidence: 99%