Introduction: The automatic determination of optimal deep brain stimulation (DBS) parameters is of great importance to improve the stimulation control of the increasingly popular segmented DBS leads. For these leads, the parameter space of stimulation settings is too large to be manually explored to its full extent in clinical practice. Thus, programming algorithms are necessary to obtain optimal stimulation settings and to reduce programming times. Recently, several such algorithms have been proposed and been positively validated in simulation studies. However, few data exist validating the computationally predicted stimulation settings against patient data.
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