A new article in the Journal of Neural Engineering details the latest results from a collaboration with Dr. Svjetlana Miocinovic and Dr. Babak Mahmoudi of Emory University to investigate new methods of automating the parameter programming of DBS systems. These systems may improve the clinical treatment of those suffering from Parkinson’s Disease or Essential Tremor by dramatically easing the programming burden on clinicians, allowing patient therapy to be updated easier and with greater frequency. Congratulations to the lead author and Emory PhD student Parisa Sarikhani for all her work on getting this paper published!
Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson’s disease and essential tremor
Parisa Sarikhani, Benjamin Ferleger, Kyle Mitchell, Jill Ostrem, Jeffrey Herron, Babak Mahmoudi, Svjetlana Miocinovic
Direct Link: https://iopscience.iop.org/article/10.1088/1741-2552/ac86a2/meta
Abstract: Objective. Deep brain stimulation (DBS) programming for movement disorders requires systematic fine tuning of stimulation parameters to ameliorate tremor and other symptoms while avoiding side effects. DBS programming can be a time-consuming process and requires clinical expertise to assess response to DBS to optimize therapy for each patient. In this study, we describe and evaluate an automated, closed-loop, and patient-specific framework for DBS programming that measures tremor using a smartwatch and automatically changes DBS parameters based on the recommendations from a closed-loop optimization algorithm thus eliminating the need for an expert clinician. Approach. Bayesian optimization which is a sample-efficient global optimization method was used as the core of this DBS programming framework to adaptively learn each patient’s response to DBS and suggest the next best settings to be evaluated. Input from a clinician was used initially to define a maximum safe amplitude, but we also implemented ‘safe Bayesian optimization’ to automatically discover tolerable exploration boundaries. Main results. We tested the system in 15 patients (nine with Parkinson’s disease and six with essential tremor). Tremor suppression at best automated settings was statistically comparable to previously established clinical settings. The optimization algorithm converged after testing 
settings when maximum safe exploration boundaries were predefined, and 
when the algorithm itself determined safe exploration boundaries. Significance. We demonstrate that fully automated DBS programming framework for treatment of tremor is efficient and safe while providing outcomes comparable to that achieved by expert clinicians.