Selection of neurostimulator parameter configurations using neural network

• US 7,252,090 B2
• Filed: 01/29/2004
• Issued: 08/07/2007
• 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): Layno; Carl
• Assistant Examiner(s): Malamud; Deborah
• Application Number:
  US10/767,922
• Publication Number:
  US 20050060010A1
• Time in Patent Office: 1,286 Days
• Field of Search: 607/5, 607/7, 607 45- 46, 607/48, 607/60, 607116-117, 607/27, 607/2, 607 55- 57, 706/48, 706/15, 600/546, 128/905, 128/925
• US Class Current: 607/2
• CPC Class Codes: A61N 1/3605 Implantable neurostimulators for stimulating central or peripheral nerve system
• CPC Class Codes: A61N 1/36071 Pain
• CPC Class Codes: A61N 1/37252 Details of algorithms or data aspects of communication system, e.g. handshaking, transmitting specific data or segmenting data
• CPC Class Codes: Y10S 128/925 Neural network

Abstract

In general, the invention is directed to a technique for selection of parameter configurations for a neurostimulator using neural networks. The technique may be employed by a programming device to allow a clinician to select parameter configurations, and then program an implantable neurostimulator to deliver therapy using the selected parameter configurations. The parameter configurations may include one or more of a variety of parameters, such as electrode configurations defining electrode combinations and polarities for an electrode set implanted in a patient. The electrode set may be carried by one or more implanted leads that are electrically coupled to the neurostimulator. 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 neural network that identifies potential optimum parameter configurations.

Claims

1. A method comprising:

selecting a first electrode configuration for a neurostimulator;

receiving an indication of observed efficacy of the first electrode configuration; and

iteratively selecting additional electrode configurations for the neurostimulator based on observed efficacies of preceding electrode configurations,

wherein iteratively selecting the additional electrode configurations comprises applying the observed efficacies of the preceding electrode configurations to a neural network,

wherein each of the electrode configurations defines a combination of two or more electrodes for delivery of neurostimulation therapy, and

wherein the neural network comprises a plurality of interconnected nodes.

2. The method of claim 1, wherein each of the electrode configurations defines polarities for electrodes in the combination.

3. The method of claim 1, wherein the electrodes are carried by two or more implanted leads.

4. The method of claim 1, wherein the electrodes are associated with different target regions within a body of a patient.

5. The method of claim 4, wherein the implanted leads are implanted proximate a portion of a spine of a patient.

6. The method of claim 1, further comprising updating the neural network based on the observed efficacies.

7. The method of claim 6, wherein updating the neural network includes modifying interconnection weights among the nodes in the neural network.

8. The method of claim 7, wherein the nodes include input nodes identifying possible combinations of two or more electrodes, output nodes identifying suggested combinations of two or more electrodes, and hidden nodes interconnecting the input nodes and the output nodes with respective interconnection weights.

9. The method of claim 1, further comprising terminating the iterative selection of the additional electrode configurations when one or more termination criteria are satisfied.

10. The method of claim 9, wherein the termination criteria include selection of one of the electrode configurations with an observed efficacy that satisfies a threshold efficacy.

11. The method of claim 1, further comprising:

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

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

12. The method of claim 11, wherein the neurostimulator is a spinal cord stimulator, and the final electrode configuration includes electrodes deployed on one more implanted spinal leads.

13. The method of claim 12, wherein the final electrode configuration defines a combination of two electrodes from a set of at least eight electrodes.

14. The method of claim 1, wherein selecting the electrode configurations includes suggesting the electrode configurations to a clinician.

15. The method of claim 1, wherein receiving an indication relating to observed efficacy includes receiving user input indicating observed efficacy.

16. A computer-readable medium comprising instructions to cause a processor to:

select a first electrode configuration for a neurostimulator;

receive an indication relating to observed efficacy of the first electrode configuration; and

iteratively select additional electrode configurations for the neurostimulator based on observed efficacies of preceding electrode configurations,

wherein the instructions that cause a processor to iteratively select additional electrode configurations comprise instructions that cause a processor to apply the observed efficacies of the preceding electrode configurations to a neural network,

wherein each of the electrode configurations defines a combination of two or more electrodes for delivery of neurostimulation therapy, and

wherein the neural network comprises a plurality of interconnected nodes.

17. The computer-readable medium of claim 16, wherein each of the electrode configurations defines polarities for electrodes in the combination.

18. The computer-readable medium of claim 16, wherein the electrodes are carried by two or more implanted leads.

19. The computer-readable medium of claim 16, wherein the electrodes are associated with different target regions within a body of a patient.

20. The computer-readable medium of claim 19, wherein the implanted leads are implanted proximate a portion of a spine of a patient.

21. The computer-readable medium of claim 16, further comprising instructions to cause the processor to update the neural network based on the observed efficacies.

22. The computer-readable medium of claim 21, wherein the instructions to cause the processor to update the neural network include instructions to cause the processor to modify interconnection weights among the nodes in the neural network.

23. The computer-readable medium of claim 22, wherein the nodes include input nodes identifying possible combinations of two or more electrodes, output nodes identifying suggested combinations of two or more electrodes, and hidden nodes interconnecting the input nodes and the output nodes with respective interconnection weights.

24. The computer-readable medium of claim 16, further comprising instructions to cause the processor to terminate the iterative selection of the additional electrode configurations when one or more termination criteria are satisfied.

25. The computer-readable medium of claim 24, wherein the termination criteria include selection of one of the electrode configurations with an observed efficacy that satisfies a threshold efficacy.

26. The computer-readable medium of claim 16, further comprising instructions to cause the processor to:

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.

27. The computer-readable medium of claim 26, wherein the neurostimulator is a spinal cord stimulator, and the final electrode configuration includes electrodes deployed on one more implanted spinal leads.

28. The computer-readable medium of claim 27, wherein the final electrode configuration defines a combination of two electrodes from a set of at least eight electrodes.

29. The computer-readable medium of claim 16, wherein the instructions that cause the processor to select the electrode configurations comprise instructions that cause the processor to suggest the electrode configurations to a clinician.

30. The computer-readable medium of claim 16, wherein the indication relating to observed efficacy includes user input relating to observed efficacy.

31. A device comprising a processor programmed to:

select a first electrode configuration for a neurostimulator;

receive an indication of observed efficacy of the first electrode configuration; and

iteratively select additional electrode configurations for the neurostimulator based on observed efficacies of preceding electrode configurations by applying the observed efficacies of the preceding electrode configurations to a neural network,

wherein each of the electrode configurations defines a combination of two or more electrodes for delivery of neurostimulation therapy, and

wherein the neural network comprises a plurality of interconnected nodes.

32. The device of claim 31, wherein each of the electrode configurations defines polarities for electrodes in the combination.

33. The device of claim 31, wherein the electrodes are carried by two or more implanted leads.

34. The device of claim 31, wherein the electrodes are associated with different target regions within a body of a patient.

35. The device of claim 34, wherein the implanted leads are implanted proximate a portion of a spine of a patient.

36. The device of claim 31, wherein the processor updates the neural network based on the observed efficacies.

37. The device of claim 36, wherein the processor updates interconnection weights among the nodes in the neural network.

38. The device of claim 37, wherein the nodes include input nodes identifying possible combinations of two or more electrodes, output nodes identifying suggested combinations of two or more electrodes, and hidden nodes interconnecting the input nodes and the output nodes with respective interconnection weights.

39. The device of claim 31, wherein the processor terminates the iterative selection of the additional electrode configurations when one or more termination criteria are satisfied.

40. The device of claim 39, wherein the termination criteria include selection of one of the electrode configurations with an observed efficacy that satisfies a threshold efficacy.

41. The device of claim 31, wherein the processor:

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.

42. The device of claim 41, wherein the neurostimulator is a spinal cord stimulator, and the final electrode configuration includes electrodes deployed on one more implanted spinal leads.

43. The device of claim 42, wherein the final electrode configuration defines a combination of two electrodes from a set of at least eight electrodes.

44. The device of claim 31, wherein the processor selects the electrode configurations by suggesting the electrode configurations to a clinician.

45. The device of claim 31, wherein the indication relating to observed efficacy includes user input relating to observed efficacy.