Model predictive control of deep brain stimulation for Parkinsonian tremor

Deep brain stimulation (DBS) is an established therapy for a variety of neurological disorders, including Parkinson's disease, essential tremor, and dystonia. Recent DBS research has pursued methods for closed-loop control to provide more effective management of symptoms, side effects, and device power consumption. Most closed-loop DBS (CLDBS) studies to date use simple threshold-based controllers to trigger DBS and, as a result, any optimization of symptoms and device power consumption is only incident. In this paper, we demonstrate the utility of an approach based on identifying patient-specific models of symptom response to DBS and using these models to formulate a model predictive control strategy for CLDBS, which explicitly solves an optimization problem. We simulate the model predictive controller for various parameters and find that this approach yields a range of performances for the competing objectives of minimizing patient symptoms and device power consumption. We examine this fundamental tradeoff using the concept of Pareto optimality and conclude with a discussion about incorporating patient, clinician, and other stakeholder preferences in the design of CLDBS systems.