Design of an ON-OFF closed loop Deep Brain Stimulator for Parkinson's Disease and Essential Tremor
thesisposted on 13.12.2012 by Ishita Basu
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
Deep Brain Stimulation (DBS) is a surgical procedure involving the implantation of a battery-operated medical device that delivers high-frequency electrical stimulation to targeted areas in the brain that control movement. DBS is a standard treatment for advanced stage movement disorders such as Parkinson's disease (PD) and Essential tremor (ET). The only FDA approved DBS system for PD and ET operates open-loop, that is, it is not adaptive to the patients' needs and disease progression over time and results in long-term unnecessary stimulation. This dissertation shows that adaptive closed-loop ON-OFF DBS is possible and proposes a simple practical algorithm to do so. This dissertation has two major contributions: A stochastic model of the neuronal dynamics of the stimulation target area is developed. By using neuronal spike data recorded during DBS surgery, the model is shown (a) to be statistically consistent with the data and (b) to outperform other simpler stochastic processes used to model neuronal spike trains. The model can be modified to account for the effect of DBS on neuronal spiking activity. In order to adaptively switch DBS stimulation ON when tremor is present, an algorithm is designed that predicts the onset of disease symptom (pathological tremor in this case) during the "DBS-OFF" period. The prediction algorithm uses a set of parameters extracted from non-invasively recorded surface EMG and acceleration signals measured from the tremor-affected extremities of PD and ET patients. The resulting algorithm is shown to successfully predict tremor onset for all trials recorded in 4 PD patients and 4 ET patients. The predictor achieves a 100% sensitivity for all trials considered, along with an overall accuracy of 85.7% for ET trials and 80.2% for PD trials. By using a chi-square test, the prediction results are shown to significantly differ from a random prediction outcome. This dissertation thus opens up a novel way of designing an adaptive ON-OFF control paradigm that can be added on to the existing DBS system.