# US 2019/0168002 A1 · Method and apparatus for optimizing spatio-temporal patterns of neurostimulation for varying conditions > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0168002 A1 - Document ID: US20190168002A1 - Status: Published application - Kind: A1 - Application number: 16/273,471 - Filed: 2019-02-12 - Published: 2019-06-06 - Earliest claimed: 2015-12-30 - Assignees: Boston Scientific Neuromodulation Corporation - Applicants: Boston Scientific Neuromodulation Corporation - Inventors: G. Karl Steinke; Michael A. Moffitt; Hemant Bokil - Web page: https://patentgrep.com/patent/US20190168002A1 ## CPC - A61N 1/36146: {specified by the stimulation parameters} - A61N 1/0551: {Spinal or peripheral nerve electrodes} - A61N 1/36062: {Spinal stimulation} - A61N 1/36071: {Pain} - A61N 1/36128: {Control systems} - A61N 1/36135: {using physiological parameters} - A61N 1/37247: {User interfaces, e.g. input or presentation means} - G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture - A61N 1/36132: {using patient feedback} - A61N 1/36139: {with automatic adjustment} - A61N 1/36185: {Selection of the electrode configuration} - A61N: ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY (measurement of bioelectric currents A61B; surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body A61B18/00; anaesthetic apparatus in general A61M; incandescent lamps H01K; infrared radiators for heating H05B) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract An example of a system for programming a neurostimulator may include a storage device and a pattern generator. The storage device may store a pattern library and one or more neuronal network models. The pattern library may include fields and waveforms of neuromodulation. The one or more neuronal network models may each be configured to allow for evaluating effects of one or more fields in combination with one or more waveforms in treating one or more indications for neuromodulation. The pattern generator may be configured to construct and approximately optimize a spatio-temporal pattern of neurostimulation and/or its building blocks for a specified range of varying conditions using at least one neuronal network model. ## Claims ### Claim 1 A system for programming a neurostimulator to deliver neurostimulation energy through a plurality of electrodes, the system comprising: a storage device configured to store: a pattern library including: a plurality of fields each specifying a spatial distribution of the neurostimulation energy across the plurality of electrodes; and a plurality of waveforms each specifying a temporal pattern of the neuromodulation energy; and one or more neuronal network models each being a computational model configured to allow for evaluating effects of one or more fields selected from the plurality of fields in combination with one or more waveforms selected from the plurality of waveforms in treating one or more indications for neuromodulation; and a pattern generator configured to generate a spatio-temporal pattern of neurostimulation specifying a sequence of one or more spatio-temporal units each including one or more fields selected from the plurality of fields in combination with one or more waveforms selected from the plurality of waveforms, the pattern generator including: a pattern editor configured to construct one or more of the plurality of fields, the plurality of waveforms, the one or more spatio-temporal units, or the spatio-temporal pattern of neurostimulation; and a pattern optimizer configured to optimize one or more of the plurality of fields, the plurality of waveforms, the one or more spatio-temporal units, or the spatio-temporal pattern of neurostimulation for a specified range of varying conditions using at least one neuronal network model of the one or more neuronal network models. ### Claim 2 (depends on 1) The system of claim 1, further comprising a programming control circuit configured to generate stimulation parameters based on the generated spatio-temporal pattern of neurostimulation, the stimulation parameters controlling the delivery of the neurostimulation energy from the neurostimulator. ### Claim 3 (depends on 1) The system of claim 1, further comprising the neurostimulator configured to deliver the of the neurostimulation energy using the generated spatio-temporal pattern of neurostimulation. ### Claim 4 (depends on 1) The system of claim 1, wherein the one or more neuronal network models comprise at least one robust model configured to allow for evaluation of one or more fields selected from the plurality of fields in combination with one or more waveforms selected from the plurality of waveforms for at least one therapeutic effect of the one or more therapeutic effects under the specified range of varying conditions. ### Claim 5 (depends on 4) The system of claim 4, wherein the pattern optimizer is configured to optimize the spatio-temporal pattern of neurostimulation for the at least one therapeutic effect under the specified range of varying conditions using the at least one robust model. ### Claim 6 (depends on 5) The system of claim 5, wherein the pattern optimizer is configured to optimize the one or more fields selected for the spatio-temporal pattern of neurostimulation using the at least one robust model for minimizing changes in the at least one therapeutic effect due to a change in the position of each of the one or more fields relative to a reference structure in the patient. ### Claim 7 (depends on 5) The system of claim 5, wherein the pattern optimizer is configured to optimize the spatio-temporal pattern of neurostimulation using the at least one robust model for minimizing changes in the at least one therapeutic effect due to a change in a sensed physiological signal or a change in a signal computed from the sensed physiological signal. ### Claim 8 (depends on 1) The system of claim 1, wherein the pattern optimizer is configured to optimize one or more of the plurality of fields, the plurality of waveforms, the one or more spatio-temporal units, or the spatio-temporal pattern of neurostimulation for the specified range of varying conditions using machine learning and decision making. ### Claim 9 (depends on 1) The system of claim 1, wherein the pattern optimizer is configured to optimize the spatio-temporal pattern of neurostimulation in a plurality of optimization steps using the at least one neuronal network model, and the specified range of varying conditions comprises a plurality of conditions corresponding to the plurality of optimization steps. ### Claim 10 (depends on 9) The system of claim 9, wherein the plurality of conditions comprises a plurality of electrode set each including a different selection of one or more electrodes from the plurality of electrodes, the neurostimulation pulses are delivered only through an electrode set of the plurality of electrode set during one step of the plurality of optimization steps, and the pattern optimizer is configured to optimize: a first field of the plurality of fields in a first step of the plurality of optimization steps during which neurostimulation pulses are delivered through only one or more electrodes specified by the first field; and one or more additional fields of the plurality of fields each in an additional step of the plurality of optimization steps during which neurostimulation pulses are delivered through only one or more electrodes specified by one or more optimized fields of the plurality of fields and one or more electrodes specified by the additional field. ### Claim 11 A method for delivering neurostimulation energy, the method comprising: providing a pattern library including: a plurality of fields each specifying a spatial distribution of the neurostimulation energy across the plurality of electrodes; and a plurality of waveforms each specifying a temporal pattern of the neuromodulation energy; providing one or more neuronal network models each being a computational model configured to allow for evaluating effects of one or more fields selected from the plurality of fields in combination with one or more waveforms selected from the plurality of waveforms in treating one or more indications for neuromodulation; and generating a spatio-temporal pattern of neurostimulation specifying a sequence of neurostimulation pulses grouped as one or more spatio-temporal units each including one or more fields selected from the plurality of fields in combination with one or more waveforms selected from the plurality of waveforms, wherein generating the spatio-temporal pattern of neurostimulation includes: constructing one or more of the plurality of fields, the plurality of waveforms, the one or more spatio-temporal units, or the spatio-temporal pattern of neurostimulation; and optimizing one or more of the plurality of fields, the plurality of waveforms, the one or more spatio-temporal units, or the spatio-temporal pattern of neurostimulation for a specified range of varying conditions using at least one neuronal network model of the one or more neuronal network models. ### Claim 12 (depends on 11) The method of claim 11, further comprising programming a neurostimulator for delivering the neurostimulation energy using the generated spatio-temporal pattern of neurostimulation. ### Claim 13 (depends on 11) The method of claim 11, wherein providing the one or more neuronal network models comprises providing at least one robust model configured to allow for evaluation of one or more fields selected from the plurality of fields in combination with one or more waveforms selected from the plurality of waveforms for at least one therapeutic effect of the one or more therapeutic effects under the specified range of varying conditions. ### Claim 14 (depends on 13) The method of claim 13, wherein optimizing the one or more of the plurality of fields, the plurality of waveforms, the one or more spatio-temporal units, or the spatio-temporal pattern of neurostimulation for the specified range of varying conditions comprises optimizing the one or more fields selected for the spatio-temporal pattern of neurostimulation using the at least one robust model for minimizing changes in the at least one therapeutic effect due to a change in the position of each of the one or more fields relative to a reference structure in the patient. ### Claim 15 (depends on 13) The method of claim 13, wherein optimizing the one or more of the plurality of fields, the plurality of waveforms, the one or more spatio-temporal units, or the spatio-temporal pattern of neurostimulation for the specified range of varying conditions comprises optimizing the one or more fields selected for the spatio-temporal pattern of neurostimulation using the at least one robust model for minimizing changes in the at least one therapeutic effect due to a change in a sensed physiological signal or a change in a signal computed from the sensed physiological signal. ### Claim 16 (depends on 11) The method of claim 11, wherein optimizing the one or more of the plurality of fields, the plurality of waveforms, the one or more spatio-temporal units, or the spatio-temporal pattern of neurostimulation for the specified range of varying conditions comprises optimizing the spatio-temporal pattern of neurostimulation in a plurality of optimization steps using the at least one neuronal network model, and the specified range of varying conditions comprises a plurality of conditions corresponding to the plurality of optimization steps. ### Claim 17 (depends on 16) The method of claim 16, wherein the plurality of conditions comprises a plurality of electrode set each including a different selection of one or more electrodes from the plurality of electrodes, and comprising delivering the neurostimulation pulses only through an electrode set of the plurality of electrode set during each step of the plurality of optimization steps. ### Claim 18 (depends on 17) The method of claim 17, wherein optimizing the one or more of the plurality of fields, the plurality of waveforms, the one or more spatio-temporal units, or the spatio-temporal pattern of neurostimulation for the specified range of varying conditions comprises optimizing two or more fields of the plurality of fields in the plurality of optimization steps, and optimizing a first field of the two or more fields in a first step of the plurality of optimization steps during which neurostimulation pulses are delivered only through one or more electrodes specified by the first field. ### Claim 19 (depends on 18) The method of claim 18, wherein optimizing the two or more fields further comprises optimizing an additional field of the two or more field in an additional step of the plurality of optimization steps during which neurostimulation pulses are delivered through only one or more electrodes specified by one or more optimized fields of the two or more fields and one or more electrodes specified by the additional field. ### Claim 20 A non-transitory machine-readable medium including instructions, which when executed by a machine, cause the machine to: connect to a pattern library including: a plurality of fields each specifying a spatial distribution of the neurostimulation energy across the plurality of electrodes; and a plurality of waveforms each specifying a temporal pattern of the neuromodulation energy; connect to one or more neuronal network models each being a computational model configured to allow for evaluating effects of one or more fields selected from the plurality of fields in combination with one or more waveforms selected from the plurality of waveforms in treating one or more indications for neuromodulation; and generate a spatio-temporal pattern of neurostimulation specifying a sequence of neurostimulation pulses grouped as one or more spatio-temporal units each including one or more fields selected from the plurality of fields in combination with one or more waveforms selected from the plurality of waveforms, wherein the generation of the spatio-temporal pattern of neurostimulation includes: constructing one or more of the plurality of fields, the plurality of waveforms, the one or more spatio-temporal units, or the spatio-temporal pattern of neurostimulation; and optimizing one or more of the plurality of fields, the plurality of waveforms, the one or more spatio-temporal units, or the spatio-temporal pattern of neurostimulation for a specified range of varying conditions using at least one neuronal network model of the one or more neuronal network models.