Boston Scientific Neuromodulation Corporation

Jianwen Gu

Bryan Allen Clark

Pramodsingh Hirasingh Thakur

Kyle Harish Srivastava

Elizabeth Mary Annoni

Rosana Esteller

Michael A Moffitt

David J Ternes

David L Perschbacher

James John Kleinedler

Que T Doan

Ismael Huertas Fernandez

Natalie A Brill

Changfang Zhu

Stephen Carcieri

Juan Gabriel Hincapie Ordonez

Hemant Bokil

Sandra Nagale

Dat Thanh Huynh

Bradley Lawrence Hershey

Tianhe Zhang

Methods and systems for testing and treating spinal cord stimulation (SCS) patients are disclosed. Patients are eventually treated with sub-perception (paresthesia free) therapy. However, supra-perception stimulation is used during 'sweet spot searching' during which active electrodes are selected for the patient. This allows sweet spot searching to occur much more quickly and without the need to wash in the various electrode combinations that are tried. After selecting electrodes using supra-perception therapy, therapy is titrated to sub-perception levels using the selected electrodes. Such sub-perception therapy has been investigated using pulses at or below 10 kHz, and it has been determined that a statistically significant correlation exists between pulse width (PW) and frequency (F) in this frequency range at which SCS patients experience significant reduction in symptoms such as back pain. Beneficially, sub-perception stimulation at such low frequencies significantly lowers power consumption in the patient's neurostimulator.

Paresthesia-free spinal cord stimulation occurring at lower frequencies and sweet spot searching using paresthesia

This document discusses, among other things, systems and methods for managing pain in a patient. A system may include sensors to sense physiological or functional signals, and a pain analyzer that generates a pain score using the sensed physiological or functional signals and a fusion model. The system includes a calibration module that calibrates the fusion model based on measurements from the sensed physiological or functional signals and a reference pain quantification corresponding to multiple pain intensities. A pain score may be generated using the calibrated fusion model. The system can additionally include a neurostimulator that controls the delivery of pain therapy by adjusting one or more stimulation parameters based on the pain score.

Patient-specific calibration of pain quantification

This document discusses, among other things, systems and methods for managing pain in a subject. A system may include one or more sensors configured to sense from the subject information corresponding to emotional reaction to pain, such as emotional expression. The emotional expression includes facial or vocal expression. A pain analyzer circuit may generate a pain score using signal metrics of facial or vocal expression extracted from the sensed information. The pain score may be output to a user or a process. The system may additionally include a neurostimulator that can adaptively control the delivery of pain therapy by automatically adjusting stimulation parameters based on the pain score.

Pain management based on emotional expression measurements

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

This document discusses, among other things, systems and methods for managing pain of a subject. A system includes a first sensor circuit to sense a first signal indicative of a functional state of the subject, a second sensor circuit to sense a second signal different from the first signal, and a controller circuit. The controller circuit may determine an operating mode of the second sensor circuit according to the sensed first signal, trigger the second senor circuit to sense the second signal under the determined operating mode, and generate a pain score using at least the second signal sensed under the determined operating mode. The pain score may be output to a patient or used for closed-loop control of a pain therapy.

Sensor-based pain management systems and methods

Systems and methods for managing pain in patient are described. A system may include sensors configured to sense physiological or functional signals, and a pain analyzer to generate signal metrics from the physiological or functional signals. The pain analyzer also generates weight factors corresponding to the signal metrics. The weight factors may indicate the signal metrics reliability in representing an intensity of the pain. The pain analyzer generates a pain score using a plurality of signal metrics and a plurality of weight factors. The pain score may be output to a user or a process. The system may additionally include an electrostimulator to generate and deliver closed-loop pain therapy according to the pain score.

Systems and methods for closed-loop pain management

This document discusses, among other things, systems and methods for managing pain of a subject. A system includes one or more physiological sensors configured to sense a physiological signal indicative of patient brain activity. The physiological signals may include an electroencephalography signal, a magnetoencephalography signal, or a brain-evoked potential. The system may extract from the brain activity signal one or more signal metrics indicative of strength or pattern of brain electromagnetic activity associated with pain, and generate a pain score using the one or more signal metrics. The pain score can be output to a patient or a process. The system may select an electrode configuration for pain-relief electrostimulation based on the pain score, and deliver a closed-loop pain therapy according to the selected electrode configuration.

Pain management based on brain activity monitoring

An example of a system for managing pain may include a pain monitoring circuit, a pain relief device, and a control circuit. The pain monitoring circuit may include a parameter analyzer and a pain score generator. The parameter analyzer may be configured to receive and analyze at least two parameters selected from a physiological parameter indicative of a physiological function or state of a patient, a functional parameter indicative of a physical activity or state of the patient, or a patient parameter including subjective information provided by the patient. The pain score generator may be configured to compute a composite pain score using an outcome of the analysis. The composite pain score may indicate a degree of the pain. The pain relief device may be configured to deliver a pain-relief therapy. The control circuit may be configured to control the delivery of the pain-relief therapy using the composite pain score.

Method and apparatus for pain management using objective pain measure

An Example of a system for providing a patient with pain management may include a sleep monitoring circuit, a pain relief device, and a control circuit. The sleep monitoring circuit may be configured to sense one or more sleep signals from the patient and to determine a sleep state of the patient using the one or more sleep signals. The one or more sleep signals may include one or more physiological signals corresponding to the sleep state of the patient. The pain relief device may be configured to deliver one or more pain relief therapies. The control circuit may be configured to control the delivery of the one or more pain relief therapies using therapy parameters and to adjust the therapy parameters based on the determined sleep state.

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Valencia, CA, United States of America

Jianwen Gu