Machine Learning for Deep Brain Stimulation

Abstract

Deep brain stimulation (DBS) is an effective treatment for a variety of neurological disorders, including Parkinson’s disease (PD). However, the success of DBS relies on selecting stimulation parameters which relieve symptoms while simultaneously avoiding stimulation-induced side-effects. Currently, DBS is programmed through a time-intensive trial-and-error process in which the clinician systematically evaluates stimulation settings, requiring hours of effort and multiple patient visits. Additionally, advances in DBS lead technology and stimulation algorithms are adding additional free parameters, further increasing the difficulty of programming these devices. This doctoral thesis advanced the programming of DBS arrays by: (1) developing the slid- ing windowed infinite Fourier transform (SWIFT), an efficient method of extracting oscillatory neural features which can be used to program DBS systems, (2) developing the Bayesian adaptive dual controller (ADC), a type of Active Learning DBS which can be used to learn optimal stimulation parameters, and (3) demonstrating the ef- ficacy of the Bayesian ADC in an animal model of PD. The primary findings of this dissertation suggest that the Bayesian ADC is capable of efficiently and autonomously learning stimulation parameters for DBS in order to optimize a selected biomarker. Furthermore, it was demonstrated that parameters learned by the Bayesian ADC performed as well as control parameters identified through a standard trial-and-error programming process. Together, these results suggest that the Bayesian ADC should be clinically translatable for tuning DBS in future studies.