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 |
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Title of host publication | 2nd International IEEE EMBS Conference on Neural Engineering |
Pages | 192-195 |
Number of pages | 4 |
Volume | 2005 |
DOIs | |
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 |
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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)