Linear predictive analysis for targeting the basal ganglia in deep brain stimulation surgeries

J. Pukala, Justin C. Sanchez, J. C. Principe, F. J. Bova, M. S. Okun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Intra-operative automated recognition of deep brain stimulation (DBS) targets from microelectrode recordings would improve the safety, efficiency, standardization, and accuracy of the surgical procedure. Our approach to the cellular classification problem is from a speech recognition perspective where linear predictive coefficient (LPC) analysis is used to model segments of thalamic and subthalamic nucleus cellular activity. We then cluster the linear prediction coefficients for three Parkinson's Disease patients and develop discriminant surfaces with an artificial neural network to generate the target classes. The methods presented here yielded a significant separation of the cell types within a two-dimensional prediction coefficient data space. The results indicate that LPC analysis for DBS targeting warrants additional study for a larger variety of deep brain structures and patients.

Original languageEnglish
Title of host publication2nd International IEEE EMBS Conference on Neural Engineering
Pages192-195
Number of pages4
Volume2005
DOIs
StatePublished - Dec 1 2005
Externally publishedYes
Event2nd International IEEE EMBS Conference on Neural Engineering, 2005 - Arlington, VA, United States
Duration: Mar 16 2005Mar 19 2005

Other

Other2nd International IEEE EMBS Conference on Neural Engineering, 2005
CountryUnited States
CityArlington, VA
Period3/16/053/19/05

Keywords

  • DBS
  • Deep Brain Stimulation
  • Linear Predictor Coefficients
  • LPC
  • Targeting

ASJC Scopus subject areas

  • Engineering(all)

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