临床颅脑EIT原始电压测量信号干扰预处理
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摘要
目的针对颅脑电阻抗断层成像临床应用中,由于测量电极与皮肤之间的接触状态不断变化导致接触阻抗和电极极化电位的改变,从而引起原始电压测量信号变化的问题。本文提出了一种效率高、性能出色的干扰预处理方法。方法利用对照实验分析了临床与理想环境下的数据差异,发现临床环境下的干扰普遍符合特定的模式,提出了线性噪声模型。为了恢复原始电压测量信号,本文设计了具有数据正则项的目标函数并利用梯度下降法进行优化。结果临床实验结果表明该算法可以有效地滤除干扰影响。结论相比于处理解调后的数据,进行原始电压测量信号干扰预处理可以有效地从根源控制数据质量,进而更好地辅助大量临床数据的分析工作。
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.
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.
引文
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[2]陈荣庆,徐灿华,董秀珍,等.电阻抗断层成像中抑制电极移动伪影方法的研究进展[J].中国医疗设备,2017,32(10):105-109.
[3]Kolehmainen V,Vauhkonen M,Karjalainen PA,et al.Assessment of errors in static electrical impedance tomography with adjacent and trigonometric current patterns[J].Physiol Meas,1997,18(4):289-303.
[4]史学涛,尤富生,霍旭阳,等.高精度电阻抗断层成像数据采集系统[J].数据采集与处理,2010,25(2):259-263.
[5]胡广书.数字信号处理:理论、算法与实现[M].2版.北京:清华大学出版社,2003.
[6]史学涛,董秀珍,秦明新,等.用于电阻抗多频及参数成像数据采集系统的正效序列解[J].第四军医大学学报,2000,21(7):164-166.
[7]魏荆华.电阻抗成像硬件系统的研究[D].天津:天津工业大学,2017.
[8]李昊庭,徐灿华,刘本源,等.稀疏加权算法与GREIT算法在颅脑电阻抗成像中的对比研究[J].中国医疗设备,2016,31(11):23-27.
[9]徐灿华.床旁电阻抗动态图像监测的重构算法及实验研究[D].西安:第四军医大学,2010.
[10]简金宝,劳译娴,晁绵涛,等.线性约束两分块非凸优化的ADMM-SQP算法[J].运筹学学报,2018,22(2):79-92.
[11]陈静思,李春.含有L1数据保真项的非凸优化脉冲噪声去除模型[J].计算机系统应用,2018,27(11):192-197.
[12]沈洁,刘晓倩,陈颖,等.非凸优化的近似束方法及对偶问题[J].重庆师范大学学报:自然科学版,2016,33(1):1-5.
[13]袁伟家,刘雨东,翟春平.一种改进的小波尺度相关阈值去噪方法[J].通信技术,2018,51(2):284-288.
[14]刘颖超,张纪元.梯度下降法[J].南京理工大学学报,1993,68(2):12-16.
[15]焦李成.神经网络系统理论[M].西安:西北工业大学出版社,1990.
[16]Rebentrost P,Schuld M,Wossnig L,et al.Quantum gradient descent and Newton’s method for constrained polynomial optimization[J].ArXiv,2016.