Digitally-Controlled Deep Brain Stimulation Lead Implant System

<p>Deep brain stimulation, the treatment of disorders by applying electrical stimulation to brain tissue, is a relatively new field of medicine with great potential to provide cures for neurological disorders. It utilizes a system very similar to a cardiac pacemaker and lead to electrically stimulate brain tissue. This electrical stimulation is programmed to disrupt or mask aberrant brain signals while not impeding the normal function of the brain. The advances in implantable pulse generators designed for deep brain stimulation have been remarkable, and the applications for deep brain stimulation continue to grow including multiple sclerosis (Berk, et al. 2002), severe psychiatric disorders (Kopell, Greenberg and Rezai 2004), and depression (Mayberg, et al. 2005). The <em>da Vinci® Surgical System</em> developed by Intuitive Surgical<em>®</em> has shown that providing surgeons with digital control of an advanced robotic surgical assistance device to be highly advantageous, however there has been minimal effort to develop a system that would provide similar advantages to deep brain stimulation surgeons. This thesis is focused on the design and utilization of a digital robotic system that will advance the safety and efficacy of the deep brain stimulation implant surgery. This is accomplished by employing current technology and custom software to control a mechanical system thereby improving relative accuracy during the deep brain stimulation lead implant procedure and providing focalized electrical stimulation. The first is achieved through digital control of motors to drive the implant procedure resulting in lead placement accuracy on a micron level and supported by computation and by FEA analysis. The latter is realized by providing the surgeon with the ability to generate curvilinear lead implant orientations which in turn concentrate electrical stimulation in a small volume of tissue with the goal of minimizing stimulation of healthy tissue and increasing battery life and supported by an electro-thermal FEA analogy.</p>

Name thesis:degree_name

MS in Mechanical Engineering

Discipline thesis:degree_discipline

Mechanical Engineering

Year dc:date.available

2011

Author dc:creator

• Phillips, Matthew Joseph

Contributors dc:contributor

• Peter Schuster

Identifier

10.15368/theses.2011.236

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-1734