Computational investigation of adaptive deep brain stimulation

Thang, Christopher Y. and Meehan, Paul A. (2015). Computational investigation of adaptive deep brain stimulation. In: Harald Loose, Ana Fred, Hugo Gamboa and Dirk Elias, Proceedings of the International Conference on Bio-inspired Systems and Signal Processing. BIOSIGNALS 2015 8th International Conference on Bio-Inspired Systems and Signal Processing, Lisbon, Portugal, (66-75). 12-15 January, 2015. doi:10.5220/0005212400660075

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Author Thang, Christopher Y.
Meehan, Paul A.
Title of paper Computational investigation of adaptive deep brain stimulation
Conference name BIOSIGNALS 2015 8th International Conference on Bio-Inspired Systems and Signal Processing
Conference location Lisbon, Portugal
Conference dates 12-15 January, 2015
Proceedings title Proceedings of the International Conference on Bio-inspired Systems and Signal Processing
Journal name BIOSIGNALS 2015 - 8th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 8th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2015
Series International Conference on Bio-Inspired Systems and Signal Processing
Publisher SciTePress
Publication Year 2015
Sub-type Fully published paper
DOI 10.5220/0005212400660075
Open Access Status Not Open Access
ISBN 9789897580697
Editor Harald Loose
Ana Fred
Hugo Gamboa
Dirk Elias
Start page 66
End page 75
Total pages 10
Collection year 2016
Language eng
Abstract/Summary Deep Brain Stimulation of the sub-thalamic nucleus (STN) has been proven to be effective at reducing symptoms of patients with Parkinson’s disease (PD). Currently an implanted pulse generator provides chronic electrical stimulation to the STN via an electrode and the stimulation parameters are chosen heuristically. Closed-loop Deep Brain Stimulation (DBS) has been proposed as an improvement to this, utilising neural signal feedback to select stimulation parameters, adjust the duration of stimulation and achieve better patient outcomes more efficiently. In this research, potential neural feedback signals were investigated using a computational simulation of the basal ganglia. It was found that the interspike-interval in the globus pallidus externus provided a possible metric for ‘on’ and ‘off’ states in Parkinson’s disease. This parameter was subsequently implemented as neural feedback in an adaptive closed-loop DBS simulation and was shown to be effective. In particular, the thalamic relaying capability was evaluated using an Error Index (EI) and the adaptive DBS was found to reduce the EI to 2%, which compared with 20% for the PD case without DBS. This was achieved using 58% of the stimulation time used during continuous DBS, indicating a large reduction in DBS energy requirements. This selection and implementation of a potential neural feedback parameter will assist in developing improved implanted DBS pulse generators.
Keyword Adaptive deep brain stimulation
Closed-loop
Neural feedback
Parkinson's disease
Q-Index Code EX
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Collections: School of Mechanical & Mining Engineering Publications
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