Response Surface Model Prediction of Deep Brain Stimulation Applied in Parkinson’s Disease Tremor

摘要

Tremor is a less investigated process/symptom of the human body. From a medical point of view, tremor is currently being assessed by empirical techniques, and a clear characterization of this signal doesn't exist. The only existing results are pathological situations, for example in Parkinson's disease where exists a criterion for evaluating Parkinson's tremors by frequency, spectral character, location, amplitude, etc., but which prove to be vague and qualitative. Deep brain stimulation (DBS) is a neurosurgical procedure involving the implantation of a medical device called a neurostimulator, which sends electrical impulses through implanted electrodes, to specific targets in the brain (brain nuclei) for the treatment of movement and neuropsychiatric disorders. The Response Surface Methodology (RSM) is a set of mathematical and statistical techniques that explore the relationship between independent and variable-response variables in order to optimize the desired response of the investigated system to explore optimal operating conditions. Some theoretical aspects and results on the application of RSM in the optimization of the DBS procedure appropriate for patients with Parkinsonian tremor, are presented in this paper.