The Closed Loop Optimization of Deep Brain Stimulation Programming

The Closed Loop Optimization of Deep Brain Stimulation Programming Singh, Ravinderjit Deep brain stimulation (DBS) is a procedure used to treat movement disorders such as Parkinson's disease. The current procedure for programming the parameters for DBS is time consuming and prone to error. The DBS programming procedure can be significantly improved using a closed-loop optimization approach. Due to recent advances in quantitative assessment metrics, the capability to translate a closed-loop optimization procedure for DBS programming from simulation to clinic has become more possible. Previous literature has presented closed-loop approaches that utilize evolutionary algorithms. It is very difficult to implement an evolutionary algorithm in the clinic because they typically require a large number of parameter evaluations. A parameter evaluation is testing how well a certain set of DBS parameters work. It is difficult to do a large number of parameter evaluations due to time constraints and patient fatigue. A response surface based closed-loop optimization approach for DBS programming is presented that has higher potential to be translated to the clinic because it requires much less parameter evaluations.

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