A neural network-based design of an on-off adaptive control for Deep Brain Stimulation in movement disorders.

The current Food and Drug Administration approved system for the treatment of tremor disorders through Deep Brain Stimulation (DBS) of the area of the brain that controls movement, operates open-loop. It does not automatically adapt to the instantaneous patient's needs or to the progression of the disease. This paper demonstrates an adaptive closed-loop controlled DBS that, after switching off stimulation, tracks few physiological signals to predict the reappearance of tremor before the patient experiences discomfort, at which point it instructs the DBS controller to switch on stimulation again. The core of the proposed approach is a Neural Network (NN) which effectively extracts tremor predictive information from non-invasively recorded surface-electromyogram(sEMG) and accelerometer signals measured at the symptomatic extremities. A simple feed-forward back-propagation NN architecture is shown to successfully predict tremor in 31 out of 33 trials in two Parkinson's Disease patients with an overall accuracy of 75.8% and sensitivity of 92.3%. This work therefore shows that closed-loop DBS control is feasible in the near future and that it can be achieved without modifications of the electrodes implanted in the brain, i.e., is backward compatible with approved DBS systems.