| Literature DB >> 34400592 |
Ashwati Krishnan1, Mats Forssell1, Zhanhong Du2, X Tracy Cui3, Gary K Fedder1, Shawn K Kelly4,5.
Abstract
Objective.We derive and demonstrate how residual voltage (RV) from a biphasic electrical stimulation pulse can be used to recognize degradation at the electrode-tissue interface.Approach.Using a first order model of the electrode-tissue interface and a rectangular biphasic stimulation current waveform, we derive the equations for RV as well as RV growth over several stimulation pulses. To demonstrate the use of RV for damage detection, we simulate accelerated damage on sputtered iridium oxide film (SIROF) electrodes using potential cycling. RV measurements of the degraded electrodes are compared against standard characterization methods of cyclic voltammetry and electrochemical impedance spectroscopy.Main results.Our theoretical discussion illustrates how an intrinsic RV arises even from perfectly balanced biphasic pulses due to leakage via the charge-transfer resistance. Preliminary data inin-vivorat experiments follow the derived model of RV growth, thereby validating our hypothesis that RV is a characteristic of the electrode-tissue interface. RV can therefore be utilized for detecting damage at the electrode. Our experimental results for damage detection show that delamination of SIROF electrodes causes a reduction in charge storage capacity, which in turn reflects a measurable increase in RV.Significance.Chronically implanted electrical stimulation systems with multi-electrode arrays have been the focus of physiological engineering research for the last decade. Changes in RV over time can be a quick and effective method to identify and disconnect faulty electrodes in large arrays. Timely diagnoses of electrode status can ensure optimal long term operation, and prevent further damage to the tissue near these electrodes.Entities:
Keywords: biphasic current stimulation; electrical stimulation; electrode damage detection; electrode health monitoring; electrode-tissue interface; residual voltage
Mesh:
Year: 2021 PMID: 34400592 PMCID: PMC9032463 DOI: 10.1088/1741-2552/ac028a
Source DB: PubMed Journal: J Neural Eng ISSN: 1741-2552 Impact factor: 5.043