A machine-learning approach to volitional control of a closed-loop deep brain stimulation system.

OBJECTIVE: Deep brain stimulation (DBS) is a well-established treatment for essential tremor, but may not be an optimal therapy, as it is always on, regardless of symptoms. A closed-loop (CL) DBS, which uses a biosignal to determine when stimulation should be given, may be better. Cortical activity is a promising biosignal for use in a closed-loop system because it contains features that are correlated with pathological and normal movements. However, neural signals are different across individuals, making it difficult to create a 'one size fits all' closed-loop system. APPROACH: We used machine learning to create a patient-specific, CL DBS system. In this system, binary classifiers are used to extract patient-specific features from cortical signals and determine when volitional, tremor-evoking movement is occurring to alter stimulation voltage in real time. MAIN
RESULTS: This system is able to deliver stimulation up to 87%-100% of the time that subjects are moving. Additionally, we show that the therapeutic effect of the system is at least as good as that of current, continuous-stimulation paradigms. SIGNIFICANCE: These findings demonstrate the promise of CL DBS therapy and highlight the importance of using subject-specific models in these systems.