Wearable sensor-driven responsive deep brain stimulation for essential tremor

Abstract

Background: Deep brain stimulation (DBS) is an effective surgical therapy for individuals with essential tremor (ET). However, DBS operates continuously, resulting in adverse effects such as postural instability or dysarthria. Continuous DBS (cDBS) also presents important practical issues including limited battery life of the implantable neurostimulator (INS). Collectively, these shortcomings impact optimal therapeutic benefit in ET. Objective: The goal of the study was to establish a physiology-driven responsive DBS (rDBS) system to provide targeted and personalized therapy based on electromyography (EMG) signals. Methods: Ten participants with ET underwent rDBS using Nexus-D, a Medtronic telemetry wand that acts as a direct conduit to the INS by modulating stimulation voltage. Two different rDBS paradigms were tested: one driven by one EMG (single-sensor) and another driven by two or more EMGs (multi-sensor). The feature(s) used in the rDBS algorithms was the pow2er in the participant's tremor frequency band derived from the sensors controlling stimulation. Both algorithms were trained on kinetic and postural data collected during DBS off and cDBS states. Results: Using established clinical scales and objective measurements of tremor severity, we confirm that both rDBS paradigms deliver equivalent clinical benefit as cDBS. Moreover, both EMG-driven rDBS paradigms delivered less total electrical energy translating to an increase in the battery life of the INS. Conclusions: The results of this study verify that EMG-driven rDBS provides clinically equivalent tremor suppression compared to cDBS, while delivering less total electrical energy. Controlling stimulation using a dynamic rDBS paradigm can mitigate limitations of traditional cDBS systems.

Keywords

• Deep brain stimulation
• Responsive stimulation
• Essential tremor
• Wearable sensors
• Electromyography