Patent US10842997

Machine learning to optimize spinal cord stimulation
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US10842997

1. A system comprising: (10) (2)

a processor; and

a memory device comprising instructions, which when executed by the processor, cause the processor to: access a patient pain metric of a subject;

use the patient pain metric as an input to an algorithm that is configured for use to identify a candidate neuromodulation parameter set for a use by a neuromodulator to deliver and a non-regular electrical waveform for neuromodulation using electrodes, wherein the non-regular electrical waveform varies over a time domain including variations in at least one of timing, size or shape of the non-regular electrical waveform and the non-regular electrical waveform varies over a space domain including variations in distribution of modulation energy fractionalized across the electrodes; and

enable the neuromodulator to treat patient pain using the candidate neuromodulation parameter set to electrically stimulate the subject using the non-regular electrical waveform that varies over the time domain and that varies over the space domain including the variations in the distribution.

2. The system of claim 1, wherein the subject is a patient. (0)

3. The system of claim 1, wherein the subject is an animal from a preclinical trial. (0)

4. The system of claim 1, wherein the algorithm comprises a genetic algorithm. (0)

5. The system of claim 1, wherein the algorithm comprises a neural network. (0)

6. The system of claim 1, wherein the system is a cloud-based system, and wherein the instructions to provide the candidate neuromodulation parameter set comprise instructions to transmit the candidate neuromodulation parameter set to a client device of the cloud-based system. (0)

7. The system of claim 1, wherein the patient pain metric is an objective pain metric. (1) (0)

9. The system of claim 1, wherein the patient pain metric is a subjective pain metric. (1) (0)

11. The system of claim 1, wherein the space domain includes at least one parameter related to fractionalization or polarity. (0)

12. The system of claim 1, wherein the candidate neuromodulation parameter set is designed to produce a pulse burst that varies pulse-by-pulse. (0)

13. The system of claim 1, wherein the algorithm is a machine learning algorithm, and the memory device further comprises instructions, which when executed by the processor, cause the processor to: (0) (2)

14. A method comprising: (5) (4)

accessing, at a computerized system, a patient pain metric of a subject;

using the patient pain metric as an input to an algorithm executing on the computerized system that is configured for use to identify a candidate neuromodulation parameter set for a use by a neuromodulator to deliver a non-regular electrical waveform for neuromodulation using electrodes, wherein the non-regular waveform varies over a time domain including variations in at least one of timing, size or shape of the waveform and the non-regular waveform varies over a space domain including variations in distribution of modulation energy fractionalized across the electrodes;

use the candidate neuromodulation parameter set to enable the neuromodulator to electrically stimulate the subject using the non-regular electrical waveform that varies over the time domain and that varies over the space domain including the variations in the distribution; and

using the neuromodulator to produce the non-regular waveform that varies over the time domain and the space domain.

15. The method of claim 14, wherein the subject is a patient. (0)

16. The method of claim 14, wherein the subject is an animal from a preclinical trial. (0)

17. The method of claim 14, wherein the algorithm comprises a genetic algorithm. (0)

18. The method of claim 14, wherein the candidate neuromodulation parameter set is designed to produce a pulse burst that varies pulse-by-pulse. (0)

19. The method of claim 14, further comprising: (0) (2)

20. A non-transitory machine-readable medium including instructions, which when executed by a machine, cause the machine to: (0) (3)

access a patient pain metric of a subject;

use the patient pain metric as an input to an algorithm that is configured for use to identify a candidate neuromodulation parameter set for a use by a neuromodulator to deliver a non-regular electrical waveform for neuromodulation using electrodes, wherein the a non-regular electrical waveform varies over a time domain including variations in at least one of timing, size or shape of the waveform and the non-regular waveform varies over a space domain including variations in distribution of modulation energy fractionalized across the electrodes; and

enable the neuromodulator to treat pain using the candidate neuromodulation parameter set by electrically stimulating the subject using the non-regular electrical waveform that varies over the time domain and that varies over the space domain including the variations in the distribution.

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Patent US10842997

Machine learning to optimize spinal cord stimulation

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