Department of Automation Science and Electric Engineering, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100083, People's Republic of China.
Sanchez Research Lab, Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112-9206, United States of America.
Physiol Meas. 2021 Jun 17;42(5). doi: 10.1088/1361-6579/ac010d.
Bioimpedance technology is experiencing an increased use to assess health in a wide range of new consumer, research and clinical applications. However, the interaction between tissues producing bioimpedance data is often unclear.This work provides a novel theoretical framework to model bioimpedance measurements of nonhomogeneous tissues. We consider five case studies to validate the usefulness of our approach against finite element model simulations.Theoretical and FEM-simulated apparent resistance and reactance data were in good agreement, with a maximum relative errors <4% and <8%, respectively.The biophysics-driven framework developed provides compact analytical expressions to model nonhomogeneous bioimpedance measurements including multiple tissues with arbitrary shape and electrical properties. This work provides a new perspective to interpret nonhomogeneous bioimpedance measurements using,, andcircuit-like topology equivalents.Our framework is a new tool to better understand and describe complex nonhomogeneous biological measurements as, for example, cardiac, brain and respiratory applications using (non)invasive electrodes.
生物阻抗技术在广泛的新的消费者、研究和临床应用中越来越多地用于评估健康状况。然而，产生生物阻抗数据的组织之间的相互作用通常不清楚。这项工作提供了一个新的理论框架，用于对非均匀组织的生物阻抗测量进行建模。我们考虑了五个案例研究，以验证我们的方法对有限元模型模拟的有效性。理论和有限元模拟的表观电阻和电抗数据非常吻合，最大相对误差分别小于 4%和 8%。所开发的生物物理驱动框架提供了紧凑的解析表达式来对非均匀生物阻抗测量进行建模，包括具有任意形状和电特性的多种组织。这项工作为使用电路拓扑等效模型来解释非均匀生物阻抗测量提供了新的视角。我们的框架是一种新的工具，可以更好地理解和描述复杂的非均匀生物测量，例如使用（非）侵入性电极的心脏、大脑和呼吸应用。
