RESUMO
OBJECTIVE: Deep brain stimulation (DBS) is a safe and established treatment for essential tremor (ET) and several other movement disorders. One approach to improving DBS therapy is adaptive DBS (aDBS), in which stimulation parameters are modulated in real time based on biofeedback from either external or implanted sensors. Previously tested systems have fallen short of translational applicability due to the requirement for patients to continuously wear the necessary sensors or processing devices, as well as privacy and security concerns. APPROACH: We designed and implemented a translation-ready training data collection system for fully implanted aDBS. Two patients chronically implanted with electrocorticography strips over the hand portion of M1 and DBS probes in the ipsilateral ventral intermediate nucleus of the thalamus for treatment of ET were recruited for this study. Training was conducted using a translation-ready distributed training procedure, allowing a substantially higher degree of control over data collection than previous works. A linear classifier was trained using this system, biased towards activating stimulation in accordance with clinical considerations. MAIN RESULTS: The clinically relevant average false negative rate, defined as fraction of time during which stimulation dropped below [Formula: see text] clinical levels during movement epochs, was 0.036. Tremor suppression, calculated through analysis of gyroscope data, was 33.2% more effective on average with aDBS than with continuous DBS. During a period of free movement with aDBS, one patient reported a slight paresthesia; patients noticed no difference in treatment efficacy between systems. SIGNIFICANCE: Here is presented the first translation-ready training procedure for a fully embedded aDBS control system for MDs and one of the first examples of such a system in ET, adding to the consensus that fully implanted aDBS systems are sufficiently mature for broader deployment in treatment of movement disorders.
