On modeling the neuronal activity in movement disorder patients by using the Ornstein Uhlenbeck process.

Mathematical models of the neuronal activity in the affected brain regions of Essential Tremor (ET) and Parkinson's Disease (PD) patients could shed light into the underlying pathophysiology of these diseases, which in turn could help develop personalized treatments including adaptive Deep Brain Stimulation (DBS). In this paper, we use an Ornstein Uhlenbeck Process (OUP) to model the neuronal spiking activity recorded from the brain of ET and PD patients during DBS stereotactic surgery. The parameters of the OUP are estimated based on Inter Spike Interval (ISI) measurements, i.

Towards closed-loop deep brain stimulation: decision tree-based essential tremor patient's state classifier and tremor reappearance predictor.

Deep Brain Stimulation (DBS) is a surgical procedure to treat some progressive neurological movement disorders, such as Essential Tremor (ET), in an advanced stage. Current FDA-approved DBS systems operate open-loop, i.e.

Pathological tremor prediction using surface electromyogram and acceleration: potential use in 'ON-OFF' demand driven deep brain stimulator design.

Objective: We present a proof of concept for a novel method of predicting the onset of pathological tremor using non-invasively measured surface electromyogram (sEMG) and acceleration from tremor-affected extremities of patients with Parkinson's disease (PD) and essential tremor (ET).

Approach: The tremor prediction algorithm uses a set of spectral (Fourier and wavelet) and nonlinear time series (entropy and recurrence rate) parameters extracted from the non-invasively recorded sEMG and acceleration signals.

Main Results: The resulting algorithm is shown to successfully predict tremor onset for all 91 trials recorded in 4 PD patients and for all 91 trials recorded in 4 ET patients.

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.