A Flexible Algorithm Framework for Closed-Loop Neuromodulation Research Systems

被引：0

作者：

Carlson, Dave ^{[1
]}

Linde, Dave ^{[1
]}

Isaacson, Ben ^{[1
]}

Afshar, Pedram ^{[1
]}

Bourget, Duane ^{[1
]}

Stanslaski, Scott ^{[1
]}

Stypulkowski, Paul ^{[1
]}

Denison, Tim ^{[1
]}

机构：

[1] Medtron Neuromodulat, Minneapolis, MN 55432 USA

来源：

2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2013年

关键词：

STIMULATION;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Modulation of neural activity through electrical stimulation of tissue is an effective therapy for neurological diseases such as Parkinson's disease and essential tremor. Researchers are exploring improving therapy through adjustment of stimulation parameters based upon sensed data. This requires classifiers to extract features and estimate patient state. It also requires algorithms to appropriately map the state estimation to stimulation parameters. The latter, known as the control policy algorithm, is the focus of this work. Because the optimal control policy algorithms for the nervous system are not fully characterized at this time, we have implemented a generic control policy framework to facilitate exploratory research and rapid prototyping of new neuromodulation strategies.

引用

页码：6146 / 6150

页数：5

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