A machine-learning approach to volitional control of a closed-loop deep brain stimulation system. (16th November 2018)

• Record Type: Journal Article
• Authors: Houston, Brady; Thompson, Margaret; Ko, Andrew; Chizeck, Howard
• Is Part Of: Journal of neural engineering. Volume 16:Number 1(2019:Feb.)
• Publication Date: 2018-11-16
• Subjects: deep-brain stimulation (DBS) -- closed-loop (CL) -- neuromodulation; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/aae67f
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: 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.