Low-Dimensional Stochastic Modeling of the Electrical Properties of Biological Tissues

被引：3

作者：

Roemer, Ulrich ^{[1
,2
]}

Schmidt, Christian ^{[3
]}

van Rienen, Ursula ^{[3
]}

Schoeps, Sebastian ^{[1
,2
]}

机构：

[1] Tech Univ Darmstadt, Inst Theorie Elektromagnet Felder, D-64289 Darmstadt, Germany

[2] Tech Univ Darmstadt, Grad Sch Computat Engn, D-64293 Darmstadt, Germany

[3] Univ Rostock, Inst Allgemeine Elektrotech, D-18051 Rostock, Germany

来源：

IEEE TRANSACTIONS ON MAGNETICS | 2017年 / 53卷 / 06期

关键词：

Biomedical engineering; principal component analysis; random processes; uncertainty; DEEP BRAIN-STIMULATION; NEURAL ACTIVATION; CONDUCTIVITY; VOLUME;

D O I：

10.1109/TMAG.2017.2668841

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Uncertainty quantification plays an important role in biomedical engineering as measurement data are often unavailable and literature data show a wide variability. Using the state-of-the-art methods, one encounters difficulties when the number of random inputs is large. This is the case, e.g., when using composite Cole-Cole equations to model random electrical properties. It is shown that how the number of parameters can be significantly reduced by the Karhunen-Loeve expansion. The low-dimensional random model is used to quantify uncertainties in the axon activation during deep brain stimulation. Numerical results for a Medtronic 3387 electrode design are given.

引用

页数：4

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