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US Patent 7184837 Selection of neurostimulator parameter configurations using bayesian networks

Patent 7184837 was granted and assigned to Medtronic on February, 2007 by the United States Patent and Trademark Office.

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Patent
Patent

Patent attributes

Current Assignee
Medtronic
Medtronic
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
7184837
Patent Inventor Names
Steven M. Goetz0
Date of Patent
February 27, 2007
Patent Application Number
10767674
Date Filed
January 29, 2004
Patent Citations Received
‌
US Patent 12076301 Engaging the cervical spinal cord circuitry to re-enable volitional control of hand function in tetraplegic subjects
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US Patent 11944821 System and method to estimate region of tissue activation
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US Patent 11957910 High density epidural stimulation for facilitation of locomotion, posture, voluntary movement, and recovery of autonomic, sexual, vasomotor, and cognitive function after neurological injury
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US Patent 11992684 System for planning and/or providing neuromodulation
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US Patent 12023492 Non invasive neuromodulation device for enabling recovery of motor, sensory, autonomic, sexual, vasomotor and cognitive function
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US Patent 11672983 Sensor in clothing of limbs or footwear
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US Patent 11672982 Control system for movement reconstruction and/or restoration for a patient
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US Patent 11691015 System for neuromodulation
0
...
Patent Primary Examiner
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Kennedy Schaetzle
Patent abstract

In general, the invention is directed to a technique for selection of parameter configurations for an implantable neurostimulator using Bayesian networks. The technique may be employed by a programming device to allow a clinician to select parameter configurations, including electrode configurations, and then program an implantable neurostimulator to deliver therapy using the selected parameter configurations. In operation, the programming device executes a parameter configuration search algorithm to guide the clinician in the selection of parameter configurations. The search algorithm relies on a Bayesian network structure that encodes conditional probabilities describing different states of the parameter set. The Bayesian network structure provides a conditional probability table that represents causal relationships between different parameter configurations. The search algorithm uses the Bayesian network structure to infer likely efficacies of possible parameter configurations based on the efficacies of parameter configurations already observed.

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