Machine learning to optimize spinal cord stimulation
An example of a system may include a processor and a memory device comprising instructions, which when executed by the processor, cause the processor to: access a patient metric of a subject; use the patient metric as an input to a machine learning algorithm, the machine learning algorithm to search a plurality of neuromodulation parameter sets and to identify a candidate neuromodulation parameter set of the plurality of neuromodulation parameter sets, the candidate neuromodulation parameter set designed to produce a non-regular waveform that varies over a time domain and a space domain; and program a neuromodulator using the candidate neuromodulation parameter set to stimulate the subject.
1. A system comprising:
a processor; and
a memory device comprising instructions, which when executed by the processor, cause the processor to:
access a pain level of a subject;
use the pain level 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 electrical waveform varies 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 in the distribution.
2. The system of claim 1 , wherein the processor is configured to use a genetic algorithm to identify the candidate neuromodulation parameter set.
3. The system of claim 1 , wherein the processor is configured to use a neural network to identify the candidate neuromodulation parameter set.
4. 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.
5. The system of claim 1 , wherein the pain level is an objective pain metric.
6. The system of claim 5 , wherein the pain level is a physiological indication sensed by a sensor worn by the subject.
7. The system of claim 1 , wherein the pain level is a subjective pain metric.
8. The system of claim 1 , wherein the non-regular waveform varies fractionalization or polarity of at least one parameter.
9. The system of claim 1 , wherein the candidate neuromodulation parameter set is designed to produce a pulse burst that varies pulse-by-pulse.
10. The system of claim 1 , wherein the processor is configured to use a machine learning algorithm, and the memory device further comprises instructions, which when executed by the processor, cause the processor to:
receive from a user, a selected parameter; and
focus the machine learning algorithm using the selected parameter.
11. The system of claim 1 , wherein the non-regular electrical waveform has waveform components that vary in at least one of timing, size or shape.
12. A method comprising:
accessing, at a computerized system, a pain level of a subject;
using the pain level 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 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 in the distribution; and
using the neuromodulator to produce the non-regular waveform that varies in the distribution.
13. The method of claim 12 , further comprising using a genetic algorithm to identify the candidate neuromodulation parameter.
14. The method of claim 12 , wherein the candidate neuromodulation parameter set is designed to produce a pulse burst that varies pulse-by-pulse.
15. The method of claim 12 , further comprising:
receiving from a user, a selected parameter; and
using the selected parameter to focus an algorithm to identify a candidate neuromodulation parameter.
16. The method of claim 12 , wherein the non-regular electrical waveform has waveform components that vary in at least one of timing, size or shape.
17. A non-transitory machine-readable medium including instructions, which when executed by a machine, cause the machine to:
access a pain level of a subject;
use the pain level 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 electrical waveform varies 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 in the distribution.
18. The non-transitory machine-readable medium of claim 17 , wherein the non-regular electrical waveform has waveform components that vary in at least one of timing, size or shape.
19. The non-transitory machine-readable medium of claim 17 , wherein a genetic algorithm is used to identify the candidate neuromodulation parameter set.
20. The non-transitory machine-readable medium of claim 17 , wherein a neural network is used to identify the candidate neuromodulation parameter set.