Investigation of a Deep Brain Stimulator (DBS) System

Abstract

A deep brain stimulator (DBS) device is a surgically implanted system that delivers electrical impulses to specific targets in the brain to treat abnormal movement disorders. A DBS is like a cardiac pacemaker, but instead of sending electrical signals to the heart, it sends them to the brain instead. When DBS leads and extension wires are exposed in the biological environment, this can adversely affect impedance and battery life, resulting in poor clinical outcomes. A posthumously extracted DBS device was evaluated using visual inspection and optical microscopy as well as electrical and mechanical tests to quantify the damage leading to its impairment. The implantable pulse generator (IPG) leads, a component of the DBS, contained cracks, delamination, exfoliations, and breakage. Some aspects of in vivo damage were observed in localized areas discussed in this paper. The duration of the time in months that the DBS was in vivo was estimated based on multiple regression analyses of mechanical property testing from prior research of pacemaker extensions. The test results of three DBS extensions, when applied to the regressions, were used to estimate the in vivo duration in months. This estimation approach may provide insight into how long the leads can function effectively before experiencing mechanical failure. Measurements of the extension coils demonstrated distortion and stretching, demonstrating the changes that may occur in vivo. These changes can alter the impedance and potentially reduce the effectiveness of the clinical treatment provided by the DBS system. Ultimately, as both DBSs and pacemakers use the same insulation and lead materials, the focus of this paper is to develop a proof of concept demonstrating that the mechanical properties measured from pacemaker extensions and leads extracted posthumously of known duration, measured in months while in vivo, can be used to predict the duration of DBS leads of unknown lifespan. The goal is to explore the validity of the proposed model using multiple regression of mechanical properties.