Application US20210052900

NEUROMODULATION THERAPY SIMULATOR
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US20210052900

1. A method of simulating neuromodulation therapy, comprising: (6) (5)

receiving a first dataset corresponding to a particular neuromodulation-therapy implant device;

receiving a second dataset corresponding to a particular neuromodulation-therapy algorithm;

receiving a third dataset corresponding to a subject-record for a particular subject;

determining, based on an execution of a neuromodulation-therapy simulator, one or more predicted performance metrics that is based on the particular neuromodulation-therapy implant device, the particular neuromodulation-therapy algorithm, and the subject-record for the particular subject, wherein the one or more predicted performance metrics correspond to one or more predicted physiological responses of the subject when the particular neuromodulation-therapy algorithm is executed by the particular neuromodulation-therapy implant device to control an electrical output of the particular neuromodulation-therapy implant device; and

outputting the one or more predicted performance metrics corresponding to the one or more predicted physiological responses of the subject.

2. The method of simulating neuromodulation therapy of claim 1, further comprising: (0) (1)

3. The method of simulating neuromodulation therapy of claim 1, wherein determining the predicted performance metrics comprises: (0) (1)

4. The method of simulating neuromodulation therapy of claim 1, wherein determining the predicted performance metrics comprises: (0) (1)

5. The method of simulating neuromodulation therapy of claim 1, wherein the predicted performance metrics further comprise predicted power consumption by the particular neuromodulation-therapy implant device, wherein the predicted power consumption is based on the particular neuromodulation-therapy implant device, the particular neuromodulation-therapy algorithm, and the subject-record for the particular subject. (0)

6. The method of claim 1, wherein the particular neuromodulation-therapy algorithm uses ensemble machine learning. (0)

7. The method of claim 1, wherein the particular neuromodulation-therapy algorithm is based on Hidden Markov Model. (0)

8. A system comprising: (6) (7)

one or more data processors; and

a non-transitory computer-readable storage medium storing instructions which, when executed by the one or more data processors, cause the one or more data processors to perform operations including:

receiving a first dataset corresponding to a particular neuromodulation-therapy implant device;

receiving a second dataset corresponding to a particular neuromodulation-therapy algorithm;

receiving a third dataset corresponding to a subject-record for a particular subject;

outputting the one or more predicted performance metrics corresponding to the one or more predicted physiological responses of the subject.

9. The system of claim 8, wherein the operations further include: (0) (1)

10. The system of claim 8, wherein determining the predicted performance metrics comprises: (0) (1)

11. The system of claim 8, wherein determining the predicted performance metrics comprises: (0) (1)

12. The system of claim 8, wherein the predicted performance metrics further comprise predicted power consumption by the particular neuromodulation-therapy implant device, wherein the predicted power consumption is based on the particular neuromodulation-therapy implant device, the particular neuromodulation-therapy algorithm, and the subject-record for the particular subject. (0)

13. The system of claim 8, wherein the particular neuromodulation-therapy algorithm uses ensemble machine learning. (0)

14. The system of claim 8, wherein the particular neuromodulation-therapy algorithm is based on Hidden Markov Model. (0)

15. A non-transitory computer-readable storage medium storing instructions which, when executed by one or more data processors, cause the one or more data processors to perform operations including: (5) (5)

receiving a first dataset corresponding to a particular neuromodulation-therapy implant device;

receiving a second dataset corresponding to a particular neuromodulation-therapy algorithm;

receiving a third dataset corresponding to a subject-record for a particular subject;

outputting the one or more predicted performance metrics corresponding to the one or more predicted physiological responses of the subject.

16. The non-transitory computer-readable storage medium of claim 15, wherein the operations further include: (0) (1)

17. The non-transitory computer-readable storage medium of claim 15, wherein determining the predicted performance metrics comprises: (0) (1)

18. The non-transitory computer-readable storage medium of claim 15, wherein determining the predicted performance metrics comprises: (0) (1)

19. The non-transitory computer-readable storage medium of claim 15, wherein the predicted performance metrics further comprise predicted power consumption by the particular neuromodulation-therapy implant device, wherein the predicted power consumption is based on the particular neuromodulation-therapy implant device, the particular neuromodulation-therapy algorithm, and the subject-record for the particular subject. (0)

20. The non-transitory computer-readable storage medium of claim 15, wherein the particular neuromodulation-therapy algorithm uses ensemble machine learning. (0)

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Application US20210052900

NEUROMODULATION THERAPY SIMULATOR

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