Data Preprocessing of Raw Voltage Measurement Signal in Brain Electrical Impedance Tomography
ZENG Yushan1, XU Canhua2, MA Hang2, LI Weichen2, FU Feng2, HUANG Liyu1, SHI Xuetao2, XIA Junying2
1. School of Life Science and Technology, Xidian University, Xi’an Shaanxi 710126, China;
2. College of Biomedical Engineering, Military Medical University of Air Force, Xi’an Shaanxi 710032, China
Abstract:Objective In the clinical application of brain electrical impedance tomography, the change of contact state between the
measuring electrode and the skin, the contact impedance and the polarization potential of the electrode change often cause the change
of the original voltage measurement signal. In this paper, an efficiency, high-precision interference pre-processing method was
proposed to improve the signal quality. Methods In response to this phenomenon, an interference pretreatment method with high
efficiency and excellent performance was proposed. The control experiment was used to analyze the difference of data distribution
between clinical and ideal environments. It was found that the interference in the clinical environment generally conformed to the
specific model, and a differentiable noise model was proposed. In order to restore the original voltage measurement signal, the
objective function with data regular term was designed and optimized by gradient descent method. Results The clinical experiment
results showed that the algorithm could effectively filter out the interference effect. Conclusion Compared with the processed data,
the original voltage measurement signal interference preprocessing can effectively control the data quality from the root source, and
thus better assist the analysis of a large number of clinical data.
曾玉姗1,徐灿华2,马航2,李蔚琛2,付峰2,黄力宇1,史学涛2,夏军营2. 临床颅脑EIT原始电压测量信号干扰预处理[J]. 中国医疗设备, 2019, 34(1): 10-13.
ZENG Yushan1, XU Canhua2, MA Hang2, LI Weichen2, FU Feng2, HUANG Liyu1, SHI Xuetao2, XIA Junying2. Data Preprocessing of Raw Voltage Measurement Signal in Brain Electrical Impedance Tomography. China Medical Devices, 2019, 34(1): 10-13.