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

doi:10.1109/CNE.2005.1419588

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 proceeding › Conference 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 language	English
Title of host publication	2nd International IEEE EMBS Conference on Neural Engineering
Pages	192-195
Number of pages	4
Volume	2005
DOIs	https://doi.org/10.1109/CNE.2005.1419588
State	Published - Dec 1 2005
Externally published	Yes
Event	2nd International IEEE EMBS Conference on Neural Engineering, 2005 - Arlington, VA, United States Duration: Mar 16 2005 → Mar 19 2005

Other

Other	2nd International IEEE EMBS Conference on Neural Engineering, 2005
Country/Territory	United States
City	Arlington, VA
Period	3/16/05 → 3/19/05

Keywords

DBS
Deep Brain Stimulation
Linear Predictor Coefficients
LPC
Targeting

ASJC Scopus subject areas

Engineering(all)

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10.1109/CNE.2005.1419588

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Pukala, J, Sanchez, JC, Principe, JC, Bova, FJ & Okun, MS 2005, Linear predictive analysis for targeting the basal ganglia in deep brain stimulation surgeries. in 2nd International IEEE EMBS Conference on Neural Engineering. vol. 2005, 1419588, pp. 192-195, 2nd International IEEE EMBS Conference on Neural Engineering, 2005, Arlington, VA, United States, 3/16/05. https://doi.org/10.1109/CNE.2005.1419588

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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.",

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AU - Sanchez, Justin C.

AU - Principe, J. C.

AU - Bova, F. J.

AU - Okun, M. S.

PY - 2005/12/1

Y1 - 2005/12/1

N2 - 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.

AB - 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.

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KW - Deep Brain Stimulation

KW - Linear Predictor Coefficients

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KW - Targeting

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BT - 2nd International IEEE EMBS Conference on Neural Engineering

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

Abstract

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Keywords

ASJC Scopus subject areas

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