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Characterizing Longitudinal Changes in the Impedance Spectra of In-Vivo Peripheral Nerve Electrodes

机译:表征体内外周神经电极阻抗谱的纵向变化

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摘要

Characterizing the aging processes of electrodes in vivo is essential in order to elucidate the changes of the electrode–tissue interface and the device. However, commonly used impedance measurements at 1 kHz are insufficient for determining electrode viability, with measurements being prone to false positives. We implanted cohorts of five iridium oxide (IrOx) and six platinum (Pt) Utah arrays into the sciatic nerve of rats, and collected the electrochemical impedance spectroscopy (EIS) up to 12 weeks or until array failure. We developed a method to classify the shapes of the magnitude and phase spectra, and correlated the classifications to circuit models and electrochemical processes at the interface likely responsible. We found categories of EIS characteristic of iridium oxide tip metallization, platinum tip metallization, tip metal degradation, encapsulation degradation, and wire breakage in the lead. We also fitted the impedance spectra as features to a fine-Gaussian support vector machine (SVM) algorithm for both IrOx and Pt tipped arrays, with a prediction accuracy for categories of 95% and 99%, respectively. Together, this suggests that these simple and computationally efficient algorithms are sufficient to explain the majority of variance across a wide range of EIS data describing Utah arrays. These categories were assessed over time, providing insights into the degradation and failure mechanisms for both the electrode–tissue interface and wire bundle. Methods developed in this study will allow for a better understanding of how EIS can characterize the physical changes to electrodes in vivo.
机译:为了阐明电极-组织界面和设备的变化,表征体内电极的老化过程至关重要。但是,常用的1 kHz阻抗测量不足以确定电极的可行性,因为测量容易产生误报。我们将五个氧化铱(IrOx)和六个铂(Pt)犹他州阵列的队列植入大鼠坐骨神经,并收集了长达12周或直到阵列失效的电化学阻抗谱(EIS)。我们开发了一种对幅度谱和相位谱的形状进行分类的方法,并将分类与可能负责的界面处的电路模型和电化学过程相关联。我们发现了氧化铱电极头金属化,铂电极头金属化,电极头金属降解,封装降解和引线中的断线的EIS特征类别。我们还将阻抗谱作为特征拟合到精细高斯支持向量机(SVM)算法中,用于IrOx和Pt倾斜阵列,其预测精度分别为95%和99%。总之,这表明这些简单且计算效率高的算法足以解释描述犹他州阵列的广泛EIS数据中的大部分方差。随着时间的推移,对这些类别进行了评估,从而深入了解了电极-组织界面和线束的退化和失效机制。这项研究中开发的方法将使人们更好地了解EIS如何表征体内电极的物理变化。

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