Analyzing the decision space of deep brain stimulation settings

Deep brain stimulation of the subthalamic nucleus (STN DBS) is a surgical procedure used to treat the debilitating symptoms of advanced Parkinson´s disease (PD); characterized by tremor, slowness of movement (bradykinesia) and involuntary movement (dyskinesia). The selection of optimal stimulation parameters is based upon subjective and qualitative clinical observations made across several programming sessions. The overall objective of our work is to quantitatively track changes in the severity of symptoms throughout the course of these sessions using wearable kinematic sensors. In this paper we present preliminary results indicating a high correlation between features derived from sensor data and clinical scores of Parkinsonian symptoms which suggests that quantitative feedback could be provided to clinicians, aiding the selection of optimal stimulation parameters.