Boston Scientific Neuromodulation Corporation

Lowe Graham Jones Pllc

Hemant Bokil

Michael A Moffitt

G Karl Steinke

Stephen Carcieri

Keith R Carlton

Richard Mustakos

Peter J Yoo

Changfang Zhu

Juan Gabriel Hincapie Ordonez

Jianwen Gu

David Blum

Dean Chen

Natalie A Brill

Mark Pierre

Ara Sarian

Sherry Lin

Kelsey Bower

Danil Tyulmankov

Rafael Carbunaru

Scott Kokones

Michael X Govea

Troy Sparks

Ljubomir Manola

Joseph Costello

Mary Kotchevar

Peter Alexander Tass

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.

Machine learning to optimize spinal cord stimulation

Methods of providing electrical neural modulation to a patient's brain are disclosed herein. The methods involve differentially modulating two or more target regions of the brain. For example, a first target region may be provided with an electrical neural modulation signal that activates that target region while a second target region is provided with an electrical neural modulation signal that suppresses or deactivates that target region. As the implantable pulse generators (IPGs) described herein include independent current sources, such differential modulation can be provided with a single IPG.

Selective electrical modulation of neural populations

Methods and systems for using sensed evoked neural responses for informing aspects of neurostimulation therapy are disclosed. Electrical signals may be recorded during the provision of electrical stimulation to a patient's neural tissue. The electrical signals may be processed and analyzed using one or more classification criteria to determine if the electrical signals contain a neural response of interest. Examples of such neural responses include evoked neural responses that are oscillatory and/or resonant in nature. If the electrical signals include such responses of interest, one or more features may be extracted from the signals and used as biomarkers for informing aspects of neurostimulation therapy, such as directing lead placement, optimizing stimulation parameters, closed-loop feedback control of stimulation, and the like. Various methods and systems described herein are particularly relevant in the context of multi-site stimulation paradigms, such as coordinated reset neuromodulation.

Neural feedback assisted DBS

Incorrect connection or mapping of leads' proximal terminals to the ports of an Implantable Stimulator Device (ISD), such as an implantable pulse generator or an external trial stimulator, is a concern, and this disclosure is directed to use of measurement and identification algorithms to either determine that leads are properly connected to their assigned ISD ports, or to determine which leads are connected to the ports even if the leads are not preassigned to the ports. Particular focus is given in the disclosed technique to assessing leads that comprise larger number of electrodes than are supported at each port, and thus have more than one proximal terminal that connect to more than one port of the ISD.

Techniques for sensing incorrect lead connection to an implantable stimulator device

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.

Systems and methods for stimulation-related biological circuit element analysis and use

A method or system for facilitating the determining and setting of stimulation parameters for programming an electrical stimulation system using closed loop programming is provided. For example, pulse generator feedback logic is executed by a processor to interface with control instructions of an implantable pulse generator by incorporating one or more machine learning engines to automatically generate a proposed set of stimulation parameter values that each affect a stimulation aspect of the implantable pulse generator, receive one or more clinical responses and automatically generate a revised set of values taking into account the received clinical responses, and repeating the automated receiving of a clinical response and adjusting the stimulation parameter values taking the clinical response into account, until or unless a stop condition is reach or the a therapeutic response is indicated within a designated tolerance.

Systems and methods for closed-loop determination of stimulation parameter settings for an electrical simulation system

Methods and systems for electrical stimulation can include obtaining a biosignal of the patient; altering at least one stimulation parameter of an electrical stimulation system in response to the biosignal; and delivering an electrical stimulation current to one or more selected electrodes of the electrical stimulation system using the at least one stimulation parameter. In some embodiments, a power spectrum is determined from the biosignal. In some embodiments, the biosignal is at least two different biosignals measured at the same or different locations on the patient and a coherence, correlation, or association between the two biosignal is determined.

Devices and methods to use power spectrum or signal association for pain management

Systems and methods for stimulation-related volume analysis of therapeutic effects and other clinical indications

A system for providing electrical stimulation to a patient includes a processor configured to: provide a time-ordered arrangement of multiple stimulation instances, where each of the stimulation instances is configured to produce a different stimulation field from each other stimulation instance in the arrangement; provide an ON/OFF switch pattern that includes alternating ON periods and OFF periods; generate an intermittent stimulation program that corresponds to repetition of the arrangement of stimulation instances with omission of each of the stimulation instances occurring during the OFF periods; and initiate a signal that provides a pulse generator with instructions that enable the pulse generator to generate stimulation according to the intermittent stimulation program using an electrical stimulation lead coupled to the pulse generator.

Systems and methods for generating intermittent stimulation using electrical stimulation systems

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.

Method and apparatus for optimizing spatio-temporal patterns of neurostimulation for varying conditions

A system for programming electrical stimulation by a lead includes a processor coupled to a display. The processor presents an interface on the display with user-selectable controls to define stimulation fields and repeating stimulation patterns for delivering the stimulation fields temporally-coordinated with each other. The user-selectable controls include a field control to define the number of stimulation fields, a location control to define locations of the stimulation fields relative to the lead, a repetition control to define a repetition frequency of the stimulation patterns, and a temporal-adjustment control to define temporal adjustments of the stimulation fields. The processor also receives selections of the user-selectable controls to define the stimulation fields and the repeating stimulation patterns; and initiate a signal that provides a pulse generator with instructions that enable the pulse generator to generate the stimulation fields according to the repeating stimulation patterns using the lead coupled to the pulse generator.

Growing community of inventors

Santa Monica, CA, United States of America

Hemant Bokil