Model predictive control of deep brain stimulation for Parkinsonian tremor

• Main Authors: Haddock, Andrew, Velisar, Anca, Herron, Jeffrey, Bronte-Stewart, Helen, Chizeck, Howard J.
• Format: Conference Proceeding
• Language: English
• Subjects: Biological system modeling; Data models; Optimization; Predictive control; Predictive models; Satellite broadcasting; Switches
• ISSN: 1948-3554
• DOI: 10.1109/NER.2017.8008364

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.