Assessment of the effects of subthalamic stimulation in Parkinson disease patients by artificial neural network

This study aims at applying an artificial neural network for the evaluation of the effects of deep brain stimulation (DBS) of the subthalamic nucleus (STN) on Parkinson disease (PD) patients with and without medication. A sample of 15 PD patients who have undergone STN DBS were evaluated under four test conditions: medication off and stimulation off (mof-sof), medication off and stimulation on (mof-son), medication on and stimulation off (mon-sof) and medication on and stimulation on (mon-son). A control group with 30 subjects was also evaluated. Principal component analysis (PCA) was applied on vertical ground reaction force (vGRF) and the first six principal component scores (PC score) were obtained in both groups. Those PCs scores were used as input in a probabilistic neural network (PNN). PNN presented satisfactory classification performance in the separation of controls and PD with 90.1% accuracy, 69.2% sensitivity and 100% specificity. The stimulation mof-son and mon-son conditions presented better results compared to mon-sof. In the mof-son condition, 41.7% were classified as normal, while further enhancement (63.3%) was given by the mon-son condition. These results indicated the potentiality of PNN to quantitatively evaluate treatment effects. Furthermore, STN DBS shows improvement on vGRF pattern in PD patients, most substantially when used with medication.