Abstract: Methods and systems for assisting the programming of stimulation parameters for deep brain stimulation (DBS) for a subject patient are described. An accumulated database comprises data from a plurality of historical fitting/programming sessions. The data can include various stimulation parameter sets that were tried for patients in the database; data relating to the position of the electrode lead with respect to anatomical features the patients' brains, for example, with respect to the alignment of their subthalamic nucleus (STN); scores indicating the therapeutic effectiveness of the stimulation parameters; and data relating to stimulation field models (SFMs) for the various stimulation parameter sets. The database may be used to predict stimulation parameter sets that are likely to be therapeutically effective for the subject patient.

Abstract: 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.

Abstract: Systems and methods for configuring neurostimulation systems to identify optimal therapies for use in a patient. Positional data for a lead implanted in the patient and anatomical data are obtained. Non-linear response structures are identified in the patient, and therapy metrics are generated using non-linear functions of the quantity of volume segments in the non-linear response structures that would be activated by a given therapy configuration including a combination of current fractionalization and current amplitude.

Abstract: 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.

Abstract: An example of a system for delivering neurostimulation may include a programming control circuit and a stimulation control circuit. The programming control circuit may be configured to generate stimulation parameters controlling delivery of the neurostimulation according to a stimulation configuration. The stimulation control circuit may be configured to specify the stimulation configuration, and may include volume definition circuitry and stimulation configuration circuitry. The volume definition circuitry may be configured to determine one or more test volumes, determine a clinical effect resulting from the one or more test volumes each being activated by the neurostimulation, and determine a target volume using the determined clinical effect. The stimulation configuration circuitry may be configured to generate the specified stimulation configuration for activating the target volume.

Abstract: A system and method for selecting leadwire stimulation parameters includes a processor iteratively performing, for each of a plurality of values for a particular stimulation parameter, each value corresponding to a respective current field: (a) shifting the current field longitudinally and/or rotationally to a respective plurality of locations about the leadwire; and (b) for each of the respective plurality of locations, obtaining clinical effect information regarding a respective stimulation of the patient tissue produced by the respective current field at the respective location; and displaying a graph plotting the clinical effect information against values for the particular stimulation parameter and locations about the leadwire, and/or based on the obtained clinical effect information, identifying an optimal combination of a selected value for the particular stimulation parameter and selected location about the leadwire at which to perform a stimulation using the selected value.

Abstract: This document discusses neurostimulation devices and methods. A neurostimulation device receives an indication of a physiological search area of a subject for a therapy mapping operation. The therapy mapping operation tests combinations of stimulation location and stimulation energy for the indicated search area. The neurostimulation device estimates one or more expected symptoms of the subject for the therapy mapping operation using symptom data for the combinations of stimulation location and stimulation energy untested during the therapy mapping operation, and presents estimations of the one or more expected symptoms for the untested combinations of stimulation location and stimulation energy.

Abstract: This document discusses neurostimulation devices, systems, and methods. A neurostimulation system receives a physiological target region for the neurostimulation and one or more optimization criteria for the neurostimulation of the target region. The neurostimulation system determines a likelihood of finding a stimulation configuration solution using a first optimization algorithm according to the physiological target region, determines a likelihood that the stimulation configuration solution is a better solution than what would be found by a second optimization algorithm and selects the first optimization algorithm or the second optimization algorithm according to the determined likelihood. The neurostimulation system recurrently changes stimulation parameters according to the selected optimization algorithm to determine the stimulation configuration solution based on the one or more optimization criteria and presenting the stimulation configuration solution to a user.

Abstract: An example of a system for delivering neurostimulation may include a programming control circuit and a stimulation control circuit. The programming control circuit may be configured to generate stimulation parameters controlling delivery of the neurostimulation according to a stimulation configuration. The stimulation control circuit may be configured to specify the stimulation configuration, and may include volume definition circuitry and stimulation configuration circuitry. The volume definition circuitry may be configured to determine one or more test volumes, determine a clinical effect resulting from the one or more test volumes each being activated by the neurostimulation, and determine a target volume using the determined clinical effect. The stimulation configuration circuitry may be configured to generate the specified stimulation configuration for activating the target volume.

Abstract: A system and method for selecting leadwire stimulation parameters includes a processor iteratively performing, for each of a plurality of values for a particular stimulation parameter, each value corresponding to a respective current field: (a) shifting the current field longitudinally and/or rotationally to a respective plurality of locations about the leadwire; and (b) for each of the respective plurality of locations, obtaining clinical effect information regarding a respective stimulation of the patient tissue produced by the respective current field at the respective location; and displaying a graph plotting the clinical effect information against values for the particular stimulation parameter and locations about the leadwire, and/or based on the obtained clinical effect information, identifying an optimal combination of a selected value for the particular stimulation parameter and selected location about the leadwire at which to perform a stimulation using the selected value.

Abstract: A system for adjusting neuromodulation parameters used by a neuromodulator operably connected to a plurality of electrodes to modulate a neural target, may comprise a translation trigger detector configured to determine that a translation trigger has occurred, a first parameter setting storage configured to store first parameter settings for use by the neuromodulator to modulate the neural target, and a neuromodulation parameter translator. The neuromodulation parameter translator may be operably connected to the translation trigger detector to automatically translate the first parameter settings into a second parameter settings in response to determining the translation trigger has occurred, and replace the first parameter settings with the second parameter settings, or store the second parameter settings in a second parameter setting storage.

Abstract: Methods and systems can facilitate identifying effective electrodes and other stimulation parameters, as well as determining whether to use cathodic and anodic stimulation. Alternately, the methods and systems may identify effective electrodes and other stimulation parameters based on preferential stimulation of different types of neural elements. These methods and systems can further facilitate programming an electrical stimulation system for stimulating patient tissue.

Abstract: A system and method for selecting leadwire stimulation parameters includes a processor iteratively performing, for each of a plurality of values for a particular stimulation parameter, each value corresponding to a respective current field: (a) shifting the current field longitudinally and/or rotationally to a respective plurality of locations about the leadwire; and (b) for each of the respective plurality of locations, obtaining clinical effect information regarding a respective stimulation of the patient tissue produced by the respective current field at the respective location; and displaying a graph plotting the clinical effect information against values for the particular stimulation parameter and locations about the leadwire, and/or based on the obtained clinical effect information, identifying an optimal combination of a selected value for the particular stimulation parameter and selected location about the leadwire at which to perform a stimulation using the selected value.

Abstract: A system may include at least one medical device and a processing system. The medical device may be configured to treat a condition by delivering a therapy to a patient. The therapy is at least partially defined using a parameter set. The processing system may be configured for use to acquire clinical effect data (CED) for the condition, acquire a medication state of medication administered to treat the condition, adjust the CED based on the medication state to provide medication-adjusted CED, and identify an adjusted parameter set for the at least one medical device to deliver the therapy based on the medication-adjusted CED. The adjusted parameter set may be communicated to a clinician (or patient or other user) as a suggestion or may be automatically implemented to update the therapy.

Abstract: A system for adjusting neuromodulation parameters used by a neuromodulator operably connected to a plurality of electrodes to modulate a neural target, may comprise a translation trigger detector configured to determine that a translation trigger has occurred, a first parameter setting storage configured to store first parameter settings for use by the neuromodulator to modulate the neural target, and a neuromodulation parameter translator. The neuromodulation parameter translator may be operably connected to the translation trigger detector to automatically translate the first parameter settings into a second parameter settings in response to determining the translation trigger has occurred, and replace the first parameter settings with the second parameter settings, or store the second parameter settings in a second parameter setting storage.

Abstract: A computer implemented system and method facilitates a cycle of generation, sharing, and refinement of volumes related to stimulation of anatomical tissue, such as brain or spinal cord stimulation. Such volumes can include target stimulation volumes, side effect volumes, and volumes of estimated activation. A computer system and method also facilitates analysis of groups of volumes, including analysis of differences and/or commonalities between different groups of volumes.

Abstract: A computer implemented system and method facilitates a cycle of generation, sharing, and refinement of volumes related to stimulation of anatomical tissue, such as brain or spinal cord stimulation. Such volumes can include target stimulation volumes, side effect volumes, and volumes of estimated activation. A computer system and method also facilitates analysis of groups of volumes, including analysis of differences and/or commonalities between different groups of volumes.

Abstract: A method for optimizing stimulation for a patient having a stimulator device such as a Deep Brain Stimulation (DBS) device is disclosed. Test stimulation is provided at initial combinations of lead positions and values of a stimulation parameter such as amplitude, neural potentials are measured for each combination, and a neural response score is determined using the measured neural potentials. A next combination of position and a value of the stimulation parameter to test is determined using the neural response scores. This process repeats iteratively until a stopping criterium is met. The neural response scores and then used determine optimal therapeutic stimulation for the patient. Neural response measurements can also be used to exclude certain positions or stimulation values during subsequent optimization testing.

Abstract: A method for programming an electrical stimulation system including at least one implantable electrical stimulation lead including a plurality of electrodes that includes receiving a target and a location of the at least one implantable electrical stimulation lead; receiving at least one stimulation criterion; for each of the at least one stimulation criterion, identifying one or more values of a cost parameter that meet the stimulation criterion, wherein the cost parameter includes a ratio of a cost of stimulating at least one defined region of tissue over a benefit of stimulating a region of the target; and providing at least one of the one or more values of the cost parameter that meet the at least one stimulation criterion to a user or the electrical stimulation system to assist in programming the electrical stimulation system.

Abstract: 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.