Selection of neurostimulator parameter configurations using decision trees

• US 7,617,002 B2
• Filed: 01/29/2004
• Issued: 11/10/2009
• Est. Priority Date: 09/15/2003
• Status: Active Grant

• Current Assignee: Medtronic Incorporated (Medtronic Plc)
• Original Assignee: Medtronic Incorporated (Medtronic Plc)
• Inventors: Goetz, Steven M.
• Primary Examiner(s): BOCKELMAN, MARK
• Application Number:
  US10/767,545
• Publication Number:
  US 20050060007A1
• Time in Patent Office: 2,112 Days
• Field of Search: 607/2, 607 46- 47, 607115-117, 607/39, 607/40, 607/45
• US Class Current: 607/116
• CPC Class Codes: A61N 1/36071 Pain
• CPC Class Codes: A61N 1/36185 Selection of the electrode configuration
• CPC Class Codes: A61N 1/37252 Details of algorithms or data aspects of communication system, e.g. handshaking, transmitting specific data or segmenting data

Abstract

A selection of parameter configurations for a neurostimulator using decision trees may be employed by a programming device to allow a clinician or other user to select parameter configurations, and then program an implantable neurostimulator to deliver therapy using the selected parameter configurations. The programming device executes a parameter configuration search algorithm to guide the clinician in selection of parameter configurations. The search algorithm relies on a decision tree to identify optimum parameter configurations. A decision tree is useful in classifying observations in a data set based upon one or more attributes or fields within the data. The data set includes parameter configurations matched with observed ratings of efficacy on patients of a similar indication. The learned attribute, on which classification occurs, will be the optimum parameter configuration for a set of rated configurations used to produce the classification. The decision trees may be especially useful in identifying electrode configurations.

Claims

1. A computer-readable medium containing instructions, said instructions comprising:

algorithmic instructions that define a decision tree that comprises a plurality of nodes and branches and defines a hierarchy of possible parameter configurations for a neurostimulator, wherein each node defines an alternative parameter configuration from each of the other nodes, and each branch links one of the nodes to another of the nodes; and

instructions to cause a processor to;

select a first parameter configuration for the neurostimulator;

receive an indication of observed efficacy of the first parameter configuration;

select a branch of the decision tree based on the indication of observed efficacy and a position of the first parameter configuration within the decision tree; and

select a second parameter configuration for the neurostimulator based on the selected branch of the decision tree.

2. The computer-readable medium of claim 1, wherein the parameter configurations include electrode configurations, each of the electrode configurations defining a combination of two or more electrodes for delivery of neurostimulation energy.

3. The computer-readable medium of claim 2, wherein each of the electrode configurations defines polarities for electrodes in the combination.

4. The computer-readable medium of claim 2, wherein the electrodes are carried by two or more implanted leads, and the electrodes are associated with different target regions within a body of a patient.

5. The computer-readable medium of claim 4, wherein the leads are implanted proximate a spine of a patient.

6. The computer-readable medium of claim 2, wherein the instructions cause the processor to iteratively select additional electrode configurations for the neurostimulator based on the indication of observed efficacy of preceding electrode configurations and positions of the preceding electrode configurations within the decision tree, and terminate the iterative selection of the additional electrode configurations when one or more termination criteria are satisfied.

7. The computer-readable medium of claim 6, wherein the termination criteria include selection of one of the electrode configurations with an observed efficacy that satisfies a threshold efficacy.

8. The computer-readable medium of claim 7, wherein the termination criteria include an iteration limit.

9. The computer-readable medium of claim 2, wherein the instructions cause the processor to:

iteratively select additional electrode configurations for the neurostimulator based on observed efficacy of preceding electrode configurations and positions of the preceding electrode configurations within the decision tree;

terminate the iterative selection of the additional electrode configurations at a final electrode configuration when one or more termination criteria are satisfied; and

program the neurostimulator to employ the final electrode configuration in delivery of neurostimulation therapy.

10. The computer-readable medium of claim 9, wherein the neurostimulator is a spinal cord stimulator, and the final electrode configuration includes electrodes deployed on one more implanted spinal leads.

11. The computer-readable medium of claim 10, wherein the final electrode configuration defines a combination of two electrodes from a set of at least eight electrodes.

12. The computer-readable medium of claim 1, wherein the first parameter configuration includes a first electrode configuration and the second parameter configuration includes a second electrode configuration, wherein the first electrode configuration is defined by a root node in the decision tree, the computer-readable medium further comprising instructions to cause the processor to expand the root node to produce a next-level node defining the second electrode configuration when the observed efficacy for the first electrode configuration does not satisfy a threshold efficacy.

13. The computer-readable medium of claim 12, further comprising instructions to cause the processor to receive an indication of the efficacy of the second electrode configuration defined by the next-level node and further expand the next-level node to produce one or more additional next-level nodes defining additional electrode configurations until one of the electrode configurations associated with one of the next-level nodes satisfies the threshold efficacy or an iteration limit is reached.

14. The computer-readable medium of claim 1, wherein the instructions cause the processor to select the first and second parameter configurations by suggesting the first and second parameter configurations to a user.

15. The computer-readable medium of claim 1, wherein the instructions cause the processor to receive an indication relating to observed efficacy by receiving user input indicating observed efficacy.

16. The computer-readable medium of claim 1, wherein the instructions cause the processor to update the decision tree based on the observed efficacy.

17. The computer-readable medium of claim 16, wherein the instructions cause the processor to update the decision tree based on the observed efficacy for a particular patient.

18. The computer-readable medium of claim 1, wherein the instructions cause the processor to apply a first decision tree to determine a neurostimulation therapy type, neurostimulation device type, lead type and symptomatic indication, and apply a second decision tree based on the determination to select the second parameter configuration.

19. The computer-readable medium of claim 1, wherein the computer-readable medium comprises one or more volatile, non-volatile, fixed, removable, magnetic, optical, or electrical media.

20. A system comprising:

a memory containing algorithmic instructions that define a decision tree, the decision tree comprising a plurality of nodes and branches and defining a hierarchy of possible parameter configurations for a neurostimulator to guide in the selection of parameter configurations, wherein each node defines an alternative parameter configuration from each of the other nodes and each branch links one of the nodes to another of the nodes; and

a processor programmed to;

select a first parameter configuration for the neurostimulator;

receive an indication of observed efficacy of the first parameter configuration;

select a branch of the decision tree based on the indication of observed efficacy and a position of the first parameter within the decision tree; and

select a second parameter configuration for the neurostimulator based on the selected branch of the decision tree.

21. The system of claim 20, wherein the parameter configurations include electrode configurations, each of the electrode configurations defining a combination of two or more electrodes for delivery of neurostimulation energy.

22. The system of claim 21, wherein each of the electrode configurations defines polarities for electrodes in the combination.

23. The system of claim 21, wherein the electrodes are carried by two or more implanted leads, and the electrodes are associated with different target regions within a body of a patient.

24. The system of claim 23, wherein the leads are implanted proximate a spine of a patient.

25. The system of claim 21, wherein the processor iteratively selects additional electrode configurations for the neurostimulator based on the indication of observed efficacy of preceding electrode configurations and positions of the preceding electrode configurations within the decision tree, and terminates the iterative selection of the additional electrode configurations when one or more termination criteria are satisfied.

26. The system of claim 25, wherein the termination criteria include selection of one of the electrode configurations with an observed efficacy that satisfies a threshold efficacy.

27. The system of claim 26, wherein the termination criteria include an iteration limit.

28. The system of claim 21, wherein the processor:

iteratively selects additional electrode configurations for the neurostimulator based on observed efficacy of preceding electrode configurations and positions of the preceding electrodes within the decision tree;

terminates the iterative selection of the additional electrode configurations at a final electrode configuration when one or more termination criteria are satisfied; and

programs the neurostimulator to employ the final electrode configuration in delivery of neurostimulation therapy.

29. The system of claim 28, wherein the neurostimulator is a spinal cord stimulator, and the final electrode configuration includes electrodes deployed on one more implanted spinal leads.

30. The system of claim 29, wherein the final electrode configuration defines a combination of two electrodes from a set of at least eight electrodes.

31. The system of claim 21, wherein the first parameter configuration includes a first electrode configuration and the second parameter configuration includes a second electrode configuration, wherein the first electrode configuration is defined by a root node in the decision tree, and the processor expands the root node to produce a next-level node defining the second electrode configuration when the observed efficacy for the first electrode configuration does not satisfy a threshold efficacy.

32. The system of claim 31, wherein the processor receives an indication of the efficacy of the second electrode configuration defined by the next-level node and further expands the next-level node to produce one or more additional next-level nodes defining additional electrode configurations until one of the electrode configurations associated with one of the next-level nodes satisfies the threshold efficacy or an iteration limit is reached.

33. The system of claim 20, wherein the processor selects the first and second parameter configurations by suggesting the first and second parameter configurations to a user.

34. The system of claim 20, wherein the processor receives an indication relating to observed efficacy by receiving user input indicating observed efficacy.

35. The system of claim 20, wherein the processor updates the decision tree based on the observed efficacy.

36. The system of claim 35, wherein the processor updates the decision tree based on the observed efficacy for a particular patient.

37. The system of claim 20, wherein the processor applies a first decision tree to determine a neurostimulation therapy type, neurostimulation device type, lead type and symptomatic indication, and applies a second decision tree based on the determination to select the second parameter configuration.