Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson's disease and essential tremor

• Authors: Parisa Sarikhani, Emory University; Benjamin Ferleger, University of Pennsylvania; Kyle Mitchell, Duke University; Jill Ostrem, University of California San Francisco; Jeffrey Herron, University of Washington; Babak Mahmoudi, Emory University; Svjetlana Miocinovic, Emory University
• Journal Title: JOURNAL OF NEURAL ENGINEERING
• Volume: Volume 19, Number 4
• Publisher: IOP Publishing Ltd | 2022-08-01
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1088/1741-2552/ac86a2
• Subjects: Health Sciences, Medicine and Surgery; Engineering, Biomedical
• Keywords: Science & Technology; Technology; Life Sciences & Biomedicine; Engineering, Biomedical; Neurosciences; Engineering; Neurosciences & Neurology; closed-loop DBS; Bayesian optimization; intelligent systems; neuromodulation; wearable sensors; OPTIMIZATION; LOOP; INTENTION

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 15.1±0.7 settings when maximum safe exploration boundaries were predefined, and 17.7±4.9 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.