Home
Resource
A study on adaptive stimulation of the basal ganglia as a treatment for Parkinsonism
PDF
PDF format is widely accepted and good for printing.
Plug-in required
PDF-1(2.34 MB)
Citation & Export
View Usage Statistics
Staff View

Citation & Export
Hide

Simple citation

Leondopulos, Stathis S.. A study on adaptive stimulation of the basal ganglia as a treatment for Parkinsonism. Retrieved from https://doi.org/doi:10.7282/T3T1542B

Export

Click here for information about Citation Management Tools at Rutgers.

Statistics
Hide

Description

Uniform TitleA study on adaptive stimulation of the basal ganglia as a treatment for Parkinsonism
NameLeondopulos, Stathis S. (author); Micheli-Tzanakou, Evangelia (chair); Bushnell, Michael (internal member); Gajic, Zoran (internal member); Caggiano, Michael (internal member); Orfanidis, Sophocles (internal member); Nowakowski, Richard (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2007
Other Date2007 (degree)
SubjectElectrical and Computer Engineering, Brain stimulation
Extentxx, 193 pages
DescriptionThe purpose of this dissertation is to design an automated system for the modification of Deep Brain Stimulation (DBS) parameters based on specific identifiers in the neuronal response of Parkinsonian patients undergoing DBS treatment. The neural response patterns are obtained from an artificial neural network consisting of dynamic neuron and synapse components and programmed to exhibit a response to pulse stimuli that resembles the activity in the subthalamic nucleus of Parkinsonian patients undergoing DBS treatment. Moreover, using pulse stimuli of varying specification, a band-pass filtered response of the network is subjected to a set of signal processing techniques including Linear Predictive Coding (LPC), Autoregressive Moving Average (ARMA) modeling, Discrete Fourier Transform (DFT), moments and higher order statistics, producing a set of results or features. Then, each feature is evaluated to determine the effectiveness, in terms of error probability, of discerning between different neuronal responses to pulse stimuli. Furthermore, a digital circuit is designed at the transistor level for computing the 1st LPC coefficient of recorded neural data and also autonomously regulating the specifications of the stimulus waveform based on the value of the computed coefficient. Also, the circuit design is optimized using a pipeline to reduce dynamic power dissipation. Moreover, it is suggested that a similar design may be useful in automating the administration of DBS as a treatment for Parkinsonism with only a minimal additional power demand.
NotePh.D.
NoteIncludes bibliographical references (p. 181-192).
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3T1542B
LanguageEnglish
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
Version 8.5.5
Rutgers University Libraries - Copyright ©2024