Parkinsonian Tremor Detection from Subthalamic Nucleus Local Field Potentials for Closed-Loop Deep Brain Stimulation

Deep Brain Stimulation (DBS) is a widely used therapy to ameliorate symptoms experienced by patients with Parkinson’s Disease (PD). Conventional DBS is continuously ON even though PD symptoms fluctuate over time leading to undesirable side-effects and high energy requirements. This study investigates the use of a Iogistic regression-based classifier to identify periods when PD patients have rest tremor exploiting Local Field Potentials (LFPs) recorded with DBS electrodes implanted in the Subthalamic Nucleus in 7 PD patients (8 hemispheres). Analyzing 36.1 minutes of data with a 512 milliseconds non-overlapping window, the classification accuracy was well above chance-level for all patients, with Area Under the Curve (AUC) ranging from 0.67 to 0.93. The features with the most discriminative ability were, in descending order, power in the 31–45 Hz, 5–7 Hz, 21–30 Hz, 46–55 Hz, and 56–95 Hz frequency bands. These results suggest that using a machine learning-based classifier, such as the one proposed in this study, can form the basis for on-demand DBS therapy for PD tremor, with the potential to reduce side-effects and lower battery consumption.