A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in XXXX .

@entity1 @entity400 @entity179 @entity191
UNASSIGNED: @entity191 is a @entity179 for which @entity1 present many symptoms, @entity400 being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the @entity1 state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the @entity1 , stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of @entity191 and particularly the @entity400 . The goal of the designed system is to detect the moments the @entity1 is suffering from a @entity400 episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten @entity191 @entity1 , who were diagnosed with @entity191 and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying @entity400 episodes. Results are favourable, reaching accuracies of higher 98.7 % in 70 % of the @entity1 .
