Identifying tremor-related characteristics of basal ganglia nuclei during movement in the Parkinsonian patient.

Local field potential (LFP) and Electromyographic (EMG) signals were recorded from 12 Parkinsonian patients with tremor-dominant symptoms as they performed passive and voluntary movements. The LFP signals were categorised into episodes of tremorous and atremorous activity (identified through EMG power spectra), then divided into delta (2-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. Modulation of LFP oscillatory activity in these frequency bands were compared between the subthalamic nucleus (STN) and the globus pallidus internus (GPi) to determine if differential tremor-related characteristics were identifiable for either target. Our results suggest that such local characteristic activity is identifiable in the STN, and thus could be a target for initial development of a closed-loop demand driven stimulator device which capitalises on such activity to trigger stimulation, even during voluntary movement activity.

Prediction of Parkinson's disease tremor onset using a radial basis function neural network based on particle swarm optimization.

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18-24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.