Skip to Main Content
This study aims at using a probabilistic neural network (PNN) for discriminating between normal and Parkinson disease (PD) subjects using as input the principal components (PCs) derived from vertical component of the ground reaction force (vGRF). The trained PNN was further used for evaluating the effects of deep brain stimulation of the subthalamic nucleus (STN DBS) on PD, with and without medication. A sample of 45 subjects (30 normal and 15 PD subjects who underwent STN DBS) was evaluated by gait analysis. PD subjects were assessed under four test conditions: without treatment (mof-sof), only with stimulation (mof-son) or medication (mon-sof), and with combined treatments (mon-son). PC analysis was applied on vGRF, where six PC scores were chosen by the broken stick test. Using a bootstrap approach for the PNN model, and the area under the receiver operating characteristic curve (AUC) as performance measurement, the first three and fifth PCs were selected as input variables. The PNN presented AUC = 3D 0.995 for classifying controls and PD subjects in the mof-sof condition. When applied to classify the PD subjects under treatment, the PNN indicated that STN DBS alone is more effective than medication, and further vGRF enhancement is obtained with combined therapies.