PARAMETER VARIATION IN NEURAL STIMULATION

BACKGROUND
Field of Invention

Embodiments of the invention relate generally to systems, devices, and methods for stimulating nerves, and more specifically relate to system, devices, and methods for electrically stimulating peripheral nerve(s) to treat various diseases and disorders, as well as systems and methods for applying stimulation waveforms for improving the therapeutic benefit, outcomes, and/or experience relating to the same.

Description of Related Art

Electrical energy can be delivered transcutaneously via electrodes on the skin surface with neurostimulation systems to stimulate peripheral nerves. Essential tremor is a common movement disorder, with growing numbers due to the aging population. Tremor in the hands and forearm is especially prevalent and problematic because it makes it difficult to write, type, eat, and drink. Disorders, including essential tremor, may be treated by pharmaceutical agents, which can cause undesired side effects. Applicant's prior treatment of tremor and other disorders has been effective in many cases (see, for example, U.S. Pat. No. 9,452,287).

SUMMARY

Embodiments of the neurostimulation system disclosed herein accommodate variability in pathological tremor characteristics including variations in tremor pathology for a user. For example, the frequency of a tremor experienced by the user is not constant over time. The neurostimulation system can deliver a stimulation waveform that varies one or more parameters, as opposed to delivering a constant value, to improve the therapeutic response of the stimulation. For example, adding variation in burst frequency may account for natural variation in pathological tremor frequency. In some cases, pathological tremor frequency can change, for example, by more than 2 Hz between tasks and by up to 32% on the same task over time within an individual subject. Calibrating burst frequency to tremor frequency can improve therapeutic effect.

In certain embodiments, stimulation parameters are agnostic for any particular individual and may be varied within generally known therapeutic ranges during the course of stimulation. Adding variation in pulse frequency may account for individual differences in the brain response to peripheral nerve stimulation. For example, the evoked response generated in the ventral intermediate nucleus of the thalamus by median nerve stimulation was maximized at a pulse frequency of 50 Hz in some subjects and 100 Hz in other subjects. By varying pulse frequency throughout these range of values, the brain response is maximized during some portion of the therapy session for every individual, which may enhance therapeutic benefit.

In various embodiments, one or more of the following nerves are treated such as the median, radial, and/or ulnar nerves in the upper extremities, tibial, saphenous, and/or peroneal nerve in the lower extremities; or the auricular vagus, tragus, trigeminal or cranial nerves on the head or ear, as non-limiting examples. Stimulation of these nerves, according to several embodiments described herein, are used to treat essential tremor, Parkinson's tremor, orthostatic tremor, and multiple sclerosis, urological disorders, gastrointestinal disorders, cardiac diseases, and mood disorders (including but not limited to depression, bipolar disorder, dysthymia, and anxiety disorder), pain syndromes (including but not limited to migraines and other headaches, trigeminal neuralgia, fibromyalgia, complex regional pain syndrome), Lyme disease, stroke, among others. Inflammatory bowel disease (such as Crohn's disease), rheumatoid arthritis, multiple sclerosis, psoriatic arthritis, psoriasis, chronic fatigue syndrome, and other inflammatory diseases are treated in several embodiments. Cardiac conditions (such as atrial fibrillation) are treated in one embodiment. Inflammatory skin conditions and immune dysfunction are also treated in some embodiments.

In some embodiments, disclosed herein is a neuromodulation system to modulate one or more peripheral nerves of an arm, hand, wrist, leg, ankle, foot, head, face, neck or ear. In one embodiment, neuromodulation comprises neuromodulation of a first peripheral nerve, a processor and a memory for storing instructions that, when executed by the processor cause the device to neuromodulate a first peripheral nerve for a prespecified amount of time and vary one or more parameters over a prespecified range of parameters at a prespecified rate of variation. Parameters, include for example, burst frequency, pulse frequency, pulse width, intensity, and/or on/off cycling. Nonimplantable stimulation via electrodes in a wearable system is provided in several embodiments. Wearable systems include devices that, for example, are placed on the upper arm, upper leg, wrist, finger, ankle, ear, face and neck.

In some embodiments, disclosed herein is a neurostimulation system to stimulate one or more peripheral nerves of an arm, hand, wrist, leg, ankle, foot, head, face, neck or ear, comprising: a first peripheral nerve electrode configured to be positioned to deliver stimulation to a first peripheral nerve; and a processor and a memory for storing instructions that, when executed by the processor cause the device to: deliver stimulation to a first peripheral nerve for a prespecified amount of time; and vary one or more parameters of the first stimulus over a prespecified range of parameters at a prespecified rate of variation, where the parameters could include burst frequency, pulse frequency, pulse width, intensity, and/or on/off cycling. In some embodiments, the varied parameter is restricted to (e.g., consists essentially of or comprises) burst frequency, the range of parameters is restricted to 3-12 Hz (e.g., 3-5, 5-8, 8-12 Hz, and overlapping ranges therein), and the rate of variation is restricted to (e.g., consists essentially of or comprises) 0.001-100 Hz/s (e.g., 0.001-0.01, 0.01-0.1, 0.1-1, 1-10, 10-100 Hz, and overlapping ranges therein). In some embodiments, the varied parameter is restricted to pulse frequency, the range of parameters is restricted to (e.g., consists essentially of or comprises) 50-150 Hz (e.g., 50-100, 100-150 Hz, and overlapping ranges therein), and the rate of variation is restricted to (e.g., consists essentially of or comprises) 0.001-10,000 Hz/s(e.g., 0.001-0.01, 0.01-0.1, 0.1-1, 1-10, 10-100,100-1,000,1,000-10,000 Hz/s). In some embodiments, the varied parameter is restricted to (e.g., consists essentially of or comprises) pulse width, the range of parameters is restricted to (e.g., consists essentially of or comprises) a minimum value from one of 100, 150, 200, 250, 300, or 350 microseconds and a maximum pulse width based on an individual's comfort level at a fixed stimulation amplitude, and the rate of variation is restricted to (e.g., consists essentially of or comprises) 0.01-10,000 microseconds per second (e.g., 0.01-0.1, 0.1-1, 1-10, 10-100, 100-1,0000,1,000-10,000 microseconds per second, and overlapping ranges therein). In some embodiments, the fixed stimulation amplitude is based on an individual's sensory level with a fixed pulse width in a range between 100-500 microseconds (e.g., 100-200, 200-300, 300-400, 400-500 microseconds, and overlapping ranges therein).

In some embodiments, the varied parameter is restricted to (e.g., consists essentially of or comprises) stimulation amplitude, the range of parameters is restricted to (e.g., consists essentially of or comprises) a minimum set to the stimulation amplitude at an individual's minimum sensory threshold and a maximum set to the stimulation amplitude at an individual's maximum comfort level, and the rate of variation is restricted to (e.g., consists essentially of or comprises) 0.001-10 mNs (e.g., 0.001-0.01, 0.01-0.1, 0.1-1, 1-10 mNs, and overlapping ranges therein). In some embodiments, the varied parameter is restricted to (e.g., consists essentially of or comprises) stimulation amplitude, the range of parameters is restricted to (e.g., consists essentially of or comprises) a minimum set to a stimulation amplitude at a pre-specified increment below an individual's minimum sensory threshold (sub-sensory) and a maximum set to the stimulation amplitude at an individual's maximum comfort level and the rate of variation is restricted to (e.g., consists essentially of or comprises) 0.001-10 mNs. In some embodiments, the pre-specified increment is one of 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.9 or 1 mA.

In some embodiments, the one or more parameters of the first stimulus comprises a first parameter and a second parameter, and wherein the first parameter and the second parameter are simultaneously varied. For example, the first parameter and the second parameter are alternately varied. In some embodiments, the first parameter and the second parameter are varied on different timescales. In some embodiments, the first parameter and the second parameter are varied based on adaptive learning, and wherein the adaptive learning employs at least one of kinematic measurements or satisfaction data. In other embodiments, combinations of timescales, kinematic data and satisfaction data are used.

In some embodiments, the varied parameter is restricted to (e.g., consists essentially of or comprises) burst frequency, the range of parameters is restricted to (e.g., consists essentially of or comprises) 3-12 Hz (e.g., 3-5, 5-8, 8-12 Hz, and overlapping ranges therein), the rate of variation is restricted to (e.g., consists essentially of or comprises) 0.001-100 Hz/s (e.g., 0.001-0.01, 0.01-0.1, 0.1-1,1-10, 10-100 Hz, and overlapping ranges therein.

In some embodiments, a neurostimulation system configured to introduce variability to enhance therapeutic response for a user. The neurostimulation system comprises a first peripheral nerve electrode configured to be positioned to deliver stimulation to a first peripheral nerve and a processor and a memory for storing instructions that, when executed by the processor cause the system to: generate a stimulation waveform configured to be delivered with the first peripheral nerve electrode for a time period; vary one or more parameters of the stimulation waveform to avoid a constant value for the one or more parameters during the time period; and deliver the generated stimulation waveform to the first peripheral nerve electrode for the time period, wherein the variation in the one or more parameters enhances therapeutic response of the stimulation compared to maintaining the one or more parameters constant over the time period.

In some embodiments, a neurostimulation system configured to introduce variability to enhance therapeutic response for a user. The neurostimulation system comprises a first peripheral nerve electrode configured to be positioned to deliver stimulation to a first peripheral nerve; and a processor and a memory for storing instructions that, when executed by the processor cause the system to: generate a stimulation waveform configured to be delivered with the first peripheral nerve electrode for a time period; and vary one or more parameters of the stimulation waveform during the time period without probing one or more characteristics of the medical condition with one or more sensors while delivering the stimulation.

In some embodiments, a neurostimulation system configured to introduce variability to enhance therapeutic response for a user. The neurostimulation system comprises a first peripheral nerve electrode configured to be positioned to deliver stimulation to a first peripheral nerve; a processor and a memory for storing instructions that, when executed by the processor cause the system to: deliver stimulation to a first peripheral nerve for a prespecified amount of time; and vary each of a first parameter and a second parameter of the delivered stimulation based on adaptive learning over a prespecified range at a prespecified rate of variation, wherein the adaptive learning employs at least one of kinematic measurements or satisfaction data.

In some embodiments, disclosed is a method of stimulating a first peripheral nerve to introduce variability to enhance therapeutic response for a user. The method comprises positioning a first peripheral nerve electrode configured to be positioned to deliver stimulation to a first peripheral nerve; generating a stimulation waveform configured to be delivered with the first peripheral nerve electrode for a time period; and delivering the generated stimulation waveform to the first peripheral nerve electrode for the time period by varying one or more parameters of the stimulation waveform to avoid a constant value for the one or more parameters during the time period, wherein the variation in the one or more parameters enhances therapeutic response of the stimulation compared to maintaining the one or more parameters constant over the time period.

In some embodiments, the one or more parameters are not correlated with characteristics of the user.

In some embodiments, the varying of the one or more parameters is configured to prevent habituation to the delivered stimulation.

In some embodiments, the varying of the one or more parameters is configured to activate neuronal populations of the nerve.

In some embodiments, the varying of the one or more parameters is configured to avoid tolerance effects by the individual.

In some embodiments, the varying of the one or more parameters is configured to resemble physiological neural signaling.

In some embodiments, the processor and the memory are further configured to, when executed by the processor, cause the system to determine the value of the varied parameter based on a prespecified probabilistic distribution.

In some embodiments, the probabilistic distribution is Gaussian.

In some embodiments, the probabilistic distribution is uniform.

In some embodiments, a neuromodulation device can comprise any one or more of the embodiments described in the disclosure.

In some embodiments, a method for performing neuromodulation on one or more nerves, comprising any one or more of the embodiments described in the disclosure.

In some embodiments, a system can comprise, not comprise, consist essentially of, or consist of any number of features as disclosed herein.

In some embodiments, a method can comprise, not comprise, consist essentially of, or consist of any number of features as disclosed herein.

Any or all of the devices described herein can be used for the treatment of depression (including but not limited to post-partum depression, depression affiliated with neurological diseases, major depression, seasonal affective disorder, depressive disorders, etc.), inflammation, Lyme disease, stroke, neurological diseases (such as Parkinson's and Alzheimer's), and gastrointestinal issues (including those in Parkinson's disease). Any or all of the devices described herein can be used for the treatment of inflammatory bowel disease (such as Crohn's disease), rheumatoid arthritis, multiple sclerosis, psoriatic arthritis, osteoarthritis, psoriasis and other inflammatory diseases. Any or all of the devices described herein can be used for the treatment of inflammatory skin conditions. Any or all of the devices described herein can be used for the treatment chronic fatigue syndrome. Any or all of the devices described herein can be used for the treatment chronic inflammatory symptoms and flare ups. Systems and methods to reduce habituation and/or tolerance to stimulation in the disorders and symptoms identified herein are provided in several embodiments by, for example, introducing variability in stimulation parameter(s) described herein.

Any or all of the devices described herein can be used for the treatment of cardiac conditions (such as atrial fibrillation). Any or all of the devices described herein can be used for the treatment of immune dysfunction. Any or all of the devices described herein can be used to stimulate the autonomic nervous system. Any or all of the devices described herein can be used to balance the sympathetic/parasympathetic nervous systems.

For purposes of summarizing the disclosure, certain aspects, advantages, and novel features are discussed herein. It is to be understood that not necessarily all such aspects, advantages, or features will be embodied in any particular embodiment of the disclosure, and an artisan would recognize from the disclosure herein a myriad of combinations of such aspects, advantages, or features.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting features of some embodiments of the invention are set forth with particularity in the claims that follow. A better understanding of the features and advantages of some embodiments of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings.

FIG. 1A illustrates a block diagram of an example neuromodulation (e.g., neurostimulation) device.

FIG. 1B illustrates communications between the neurostimulation device of FIG. 1A and a user interface device over a communication link.

FIG. 2A illustrates a block diagram of an embodiment of a device and system that provides peripheral nerve stimulation and senses a biological or kinematic measure and/or receives user satisfaction data that is used to customize or modify the delivery of an electrical stimulus.

FIG. 2B illustrates a block diagram of an embodiment of a controller that can be implemented with the hardware components described with respect to FIGS. 1A, 1B, and 2A.

FIG. 2C schematically illustrates an embodiment of a neuromodulation device and base station.

FIGS. 3A-B illustrate examples of how stimulation parameters (e.g., burst frequency and pulse frequency) are varied between two or more prespecified values as stimulation is alternated across two nerves (e.g., median and radial nerve).

FIGS. 4A-B illustrate examples of how stimulation parameters (e.g., amplitude and pulse width) are varied between two or more prespecified values as stimulation is alternated across two nerves (e.g., median and radial nerve).

FIGS. 5A-B illustrate multiple examples of stimulation patterns with prespecified on/off periods as stimulation is alternated across two nerves (e.g., median and radial nerve).

FIG. 6A illustrates an example of a ramping variation of the burst frequency parameter. The burst frequency linearly ramps from 3 Hz to 12 Hz in time period of 2 seconds, which results in a rate of change of 4.5 Hz/s.

FIG. 6B illustrates an example of a ramping variation of the burst frequency parameter. The burst frequency linearly ramps from 3 Hz to 3.4 Hz in time period of 5 seconds, which results in a rate of change of 0.08 Hz/s.

FIG. 7 illustrate an example of how multiple stimulation parameters (e.g., parameters A and B) are simultaneously varied between two or more prespecified values as stimulation is applied to a nerve (e.g., median or radial nerve).

FIG. 8 illustrate an example of how multiple stimulation parameters (e.g., parameters A and B) are varied by alternately changing each parameter between two or more prespecified values as stimulation is applied to a nerve (e.g., median or radial nerve).

FIG. 10 illustrates a flow chart of an embodiment of a process for varying one or more parameters of a stimulus over a prespecified range of parameters at a prespecified rate of variation.

FIG. 11 illustrates a flow chart of an embodiment of a process for simultaneously varying multiple stimulation parameters (e.g., parameters A and B) between two or more prespecified values as stimulation is applied to a nerve (e.g., median or radial nerve).

FIG. 12 illustrates a flow chart of an embodiment of a process for alternately varying multiple stimulation parameters (e.g., parameters A and B) between two or more prespecified values as stimulation is applied to a nerve (e.g., median or radial nerve).

FIG. 14 illustrates an architecture for determining a method that varies multiple stimulation parameters based on adaptive learning.

DETAILED DESCRIPTION

Disclosed herein are devices configured for providing neuromodulation (e.g., neurostimulation). The neuromodulation (e.g., neurostimulation) devices provided herein may be configured to stimulate peripheral nerves of a user. The neuromodulation (e.g., neurostimulation) devices may be configured to transcutaneously transmit one or more neuromodulation (e.g., neurostimulation) signals across the skin of the user. In many embodiments, the neuromodulation (e.g., neurostimulation) devices are wearable devices configured to be worn by a user. The user may be a human, another mammal, or other animal user. The neuromodulation (e.g., neurostimulation) system could also include signal processing systems and methods for enhancing diagnostic and therapeutic protocols relating to the same. In some embodiments, the neuromodulation (e.g., neurostimulation) device is configured to be wearable on an upper extremity of a user (e.g., a wrist, forearm, arm, and/or finger(s) of a user). In some embodiments, the device is configured to be wearable on a lower extremity (e.g., ankle, calf, knee, thigh, foot, and/or toes) of a user. In some embodiments, the device is configured to be wearable on the head or neck (e.g., forehead, ear, neck, nose, and/or tongue). In several embodiments, dampening or blocking of nerve impulses and/or neurotransmitters are provided. In some embodiments, nerve impulses and/or neurotransmitters are enhanced. In some embodiments, the device is configured to be wearable on or proximate an ear of a user, including but not limited to auricular neuromodulation (e.g., neurostimulation) of the auricular branch of the vagus nerve, for example. The device could be unilateral or bilateral, including a single device or multiple devices connected with wires or wirelessly. In some embodiments, features disclosed for example in U.S. Pat. No. 9,452,287 to Rosenbluth et al., U.S. Pub. No. 2019/0001129 to Rosenbluth et al., U.S. Pat. No. 9,802,041 to Wong et al., and U.S. Pub. No. 2018/0169400 to Wong et al., each of which are hereby incorporated by reference in their entireties, can be combined with systems and methods as disclosed herein.

In several embodiments, neuromodulation systems and methods are provided that enhance or inhibit nerve impulses and/or neurotransmission, and/or modulate excitability of nerves, neurons, neural circuitry, and/or other neuroanatomy that affects activation of nerves and/or neurons. For example, neuromodulation (e.g., neurostimulation) can include one or more of the following effects on neural tissue: depolarizing the neurons such that the neurons fire action potentials; hyperpolarizing the neurons to inhibit action potentials; depleting neuron ion stores to inhibit firing action potentials; altering with proprioceptive input; influencing muscle contractions; affecting changes in neurotransmitter release or uptake; and/or inhibiting firing.

In some embodiments, wearable systems and methods as disclosed herein can advantageously be used to identify whether a treatment is effective in significantly reducing or preventing a medical condition, including but not limited to tremor severity. Wearable sensors can advantageously monitor, characterize, and aid in the clinical management of hand tremor as well as other medical conditions including those disclosed elsewhere herein. Not to be limited by theory, clinical ratings of medical conditions, e.g., tremor severity can correlate with simultaneous measurements of wrist motion using inertial measurement units (IMUs). For example, tremor features extracted from IMUs at the wrist can provide characteristic information about tremor phenotypes that may be leveraged to improve diagnosis, prognosis, and/or therapeutic outcomes. Kinematic measures can correlate with tremor severity, and machine learning algorithms incorporated in neuromodulation systems and methods as disclosed for example herein can predict the visual rating of tremor severity.

In some embodiments, disclosed herein is a neuromodulation system to modulate one or more peripheral nerves of an arm, hand, wrist, leg, ankle, foot, head, face, neck or ear. In one embodiment, neuromodulation comprises neuromodulation of a first peripheral nerve, a processor and a memory for storing instructions that, when executed by the processor cause the device to neuromodulate a first peripheral nerve for a prespecified amount of time and vary one or more parameters over a prespecified range of parameters at a prespecified rate of variation. Parameters, include for example, burst frequency, pulse frequency, pulse width, intensity, and/or on/off cycling. Nonimplantable stimulation via electrodes is provided in several embodiments. Stimulation may also be accomplished via an implantable system or a combination of an implantable element and nonimplantable system. Denervation may also be accomplished in some embodiments.

In some embodiments, the one or more parameters of the first stimulus comprises a first parameter and a second parameter, and wherein the first parameter and the second parameter are varied on different timescales. In some embodiments, the one or more parameters of the first stimulus comprises a first parameter and a second parameter, wherein the first parameter and the second parameter are varied based on adaptive learning, and wherein the adaptive learning employs at least one of kinematic measurements or satisfaction data. In some embodiments, disclosed herein is a method of stimulating one or more peripheral nerves of an arm, hand, wrist, leg, ankle, foot, head, face, neck or ear with a neurostimulation device, comprising: positioning a first peripheral nerve electrode to deliver stimulation to a first peripheral nerve; delivering stimulation to a first peripheral nerve for a prespecified amount of time; and/or varying one or more parameters of the first stimulus over a prespecified range of parameters at a prespecified rate of variation, where the parameters could include burst frequency, pulse frequency, pulse width, intensity, and/or on/off cycling. In some embodiments, the varied parameter is restricted to (e.g., consists essentially of or comprises) burst frequency and the rate of variation is restricted to (e.g., consists essentially of or comprises) 0.001-100 Hz/s and the range is set by: measuring motion of the patient's extremity using the one or more biomechanical sensors to generate motion data; determining tremor frequency from the motion data; and setting the range across a0.1, 0.2, 0.25, 0.3, 0.4, 0.5,1,1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, or 6 Hz or more or less window centered on the measured tremor frequency. In some embodiments, the varied parameter is restricted to (e.g., consists essentially of or comprises) pulse width and the rate of variation is restricted to (e.g., consists essentially of or comprises) 0.01-10,000 microseconds per second, and the range is set by setting pulse width to 300 microseconds, increasing and setting stimulation amplitude to an individual's minimum sensory threshold; increasing pulse width to an individual's maximum level of comfort, recording the pulse width at maximum level of comfort, and setting the minimum range value to 300 microseconds, and the maximum range value to the individual's pulse width at maximum level of comfort. In some embodiments, the varied parameter is restricted to (e.g., consists essentially of or comprises) stimulation amplitude and the rate of variation and the rate of variation is restricted to (e.g., consists essentially of or comprises) 0.001-10 mA/s, and the range is set by: increasing the stimulation amplitude to an individual's minimum sensory threshold, setting the minimum range value to this minimum sensory threshold, increasing the stimulation amplitude to an individual's maximum comfort level, and setting the maximum range value to this maximum comfort level. In some embodiments, the varied parameter is restricted to (e.g., consists essentially of or comprises) stimulation amplitude and the rate of variation and the rate of variation is restricted to (e.g., consists essentially of or comprises) 0.001-10 mA/s, and the range is set by: increasing the stimulation amplitude to an individual's minimum sensory threshold, setting the minimum range value to a value that is 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.9, or 1 mA below this minimum sensory threshold, increasing the stimulation amplitude to an individual's maximum comfort level, and setting the maximum range value to this maximum comfort level. In some embodiments, the one or more parameters of the first stimulus comprises a first parameter and a second parameter, and wherein the first parameter and the second parameter are simultaneously varied. In some embodiments, the one or more parameters of the first stimulus comprises a first parameter and a second parameter, and wherein the first parameter and the second parameter are alternately varied. In some embodiments, the one or more parameters of the first stimulus comprises a first parameter and a second parameter, and wherein the first parameter and the second parameter are varied on different timescales. In some embodiments, the one or more parameters of the first stimulus comprises a first parameter and a second parameter, wherein the first parameter and the second parameter are varied based on adaptive learning, and wherein the adaptive learning employs at least one of kinematic measurements or satisfaction data.

FIG. 1A illustrates a block diagram of an example neuromodulation (e.g., neurostimulation) device 100. The device 100 includes multiple hardware components which are capable of or programmed to provide therapy across the skin of the user. As illustrated in FIG. 1A, some of these hardware components may be optional as indicated by dashed blocks. In some instances, the device 100 may only include the hardware components that are required for stimulation therapy. The hardware components are described in more detail below.

The device 100 can include one or more electrodes 102 for providing neurostimulation signals. In some instances, the device 100 is configured for transcutaneous use only and does not include any percutaneous or implantable components. In some embodiments, the electrodes 102 can be dry electrodes 102. In some embodiments, water or gel can be applied to the dry electrode 102 or skin to improve conductance. In some embodiments, the electrodes 102 do not include any hydrogel material, adhesive, or the like.

The device 100 can further include stimulation circuitry 104 for generating signals that are applied through the electrode(s) 102. In certain embodiments, the signals can vary in, for example, frequency, phase, timing, amplitude, on/off cycling, or offsets. The device 100 can also include power electronics 106 for providing power to the hardware components. For example, the power electronics 106 can include a battery.

The device 100 can include one or more hardware processors 108. The hardware processors 108 can include microcontrollers, digital signal processors, application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. In an embodiment, all of the processing discussed herein is performed by the hardware processor(s) 108. The memory 110 can store data specific to patient and processes as discussed below.

In the illustrated figure, the device 100 can include one or more sensors (e.g., inertial measurement unit (IMU)) 112. As shown in the figure, the sensor(s) 112 may be optional. Sensors 112 could include, for example, biomechanical sensors configured to, for example, measure motion, and/or bioelectrical sensors (e.g., EMG, EEG, and/or nerve conduction sensors). Sensors can include, for example, cardiac activity sensors (e.g., ECG, PPG), skin conductance sensors (e.g., galvanic skin response, electrodermal activity), and motion sensors (e.g., accelerometers, gyroscopes). The one or more sensors 112 may include an audio sensor, including but not limited to a microphone, audio transducer, or accelerometer, configured to measure biological processes, such as breathing, talking, or repetitive motion. Sensors, in some embodiments, sense parameters that are used to optimize neurostimulation and facilitate the introduction of variability in stimulation parameter(s) to reduce tolerance and/or habituation to the neurostimulation. As an example, EEG signals, brain activity and/or neuronal activity may be used in this manner. In one embodiment, variation in one or more parameters may be configured/introduced to generate a natural or desired characteristic of brain or neuronal activity over a time period for the treatment of movement, inflammatory, neurological and psychiatric disorders.

In some embodiments, a tremor signal can be calculated based on input from the one or more of the sensors 112. The tremor signal is a representation of the tremulous activity generated in the brain and motor nerves that causes tremulous muscle activation leading to tremor in the hands, head, neck, legs, feet, and vocal cords.

In some embodiments, the sensor (e.g., IMU) 112 can include one or more of a gyroscope, accelerometer, and magnetometer. The sensor 112 can be affixed or integrated with the neuromodulation (e.g., neurostimulation) device 100. In an embodiment, the sensor 112 is an off the shelf component. In addition to its ordinary meaning, the sensor 112 can also include specific components as discussed below. For example, the sensor 112 can include one more sensors capable of collecting motion data. In an embodiment, the sensor 112 includes an accelerometer. In some embodiments, the sensor 112 can include multiple accelerometers to determine motion in multiple axes. Furthermore, the sensor 112 can also include one or more gyroscopes and/or magnetometer in additional embodiments. Since the sensor 112 can be integrated with the neurostimulation device 100, the sensor 112 can generate data from its sensors responsive to motion, movement, or vibration felt by the device 100. Furthermore, when the device 100 with the integrated sensor 112 is worn by a user, the sensor 112 can enable detection of voluntary and/or involuntary motion of the user.

The device 100 can optionally include user interface components, such as a feedback generator 114 and a display 116. The display 116 can provide instructions or information to users relating to calibration or therapy. The display 116 can also provide alerts, such an indication of response to therapy, for example. Alerts may also be provided using the feedback generator 114, which can provide haptic feedback to the user, such as upon initiation or termination of stimulation, for reminder alerts, to alert the user of a troubleshooting condition, to perform a tremor inducing activity to measure tremor motion, among others. Accordingly, the user interface components, such as the feedback generator 114 and the display 116 can provide audio, visual, and haptic feedback to the user. In certain embodiments, the feedback generator 114 and/or display 116 is configured for the user to provide satisfaction data to the device 100.

Furthermore, the device 100 can include communications hardware 118 for wireless or wired communication between the device 100 and an external system, such as the user interface device 150 discussed below. The communications hardware 118 can include an antenna. The communications hardware 118 can also include an Ethernet or data bus interface for wired communications.

While the illustrated figure shows several components of the device 100, some of these components are optional and not required in all embodiments of the device 100. In some embodiments, a system can include a diagnostic device or component that does not include neuromodulation functionality. The diagnostic device could be a companion wearable device connected wirelessly through a connected cloud server, and include, for example, sensors such as cardiac activity, skin conductance, and/or motion sensors as described elsewhere herein.

In some embodiments, the device 100 can also be configured to deliver one, two or more of the following: magnetic, vibrational, mechanical, thermal, ultrasonic, or other forms of stimulation instead of, or in addition to electrical stimulation. Such stimulation can be delivered via one, two, or more electrodes in contact with, or proximate the skin surface of the patient. However, in some embodiments, the device 100 is configured to only deliver electrical stimulation, and is not configured to deliver one or more of magnetic, vibrational, mechanical, thermal, ultrasonic, or other forms of stimulation.

Although several neurostimulation devices 100 are described herein, in some embodiments nerves are modulated non-invasively to achieve neuro-inhibition. Neuro-inhibition can occur in a variety of ways, including but not limited to hyperpolarizing the neurons to inhibit action potentials and/or depleting neuron ion stores to inhibit firing action potentials. This can occur in some embodiments via, for example, anodal or cathodal stimulation, low frequency stimulation (e.g., less than about 5 Hz in some cases), or continuous or intermediate burst stimulation (e.g., theta burst stimulation). In some embodiments, the wearable devices have at least one implantable portion, which may be temporary or more long term. In many embodiments, the devices are entirely wearable and non-implantable.

FIG. 1B illustrates communications between the neurostimulation device 100 and a user interface device 150 over a communication link 130. The communication link 130 can be wired or wireless. The neuromodulation (e.g., neurostimulation) device 100 is capable of communicating and receiving instructions from the user interface device 150. The user interface device 150 can include a computing device. In some embodiments, the user interface device 150 is a mobile computing device, such as a mobile phone, a smartwatch, a tablet, or a wearable computer. The user interface device 150 can also include server computing systems that are remote from the neurostimulation device 100. In certain embodiments, the user interface device 150 can include a hardware processor(s) 152, a memory 154, a display 156, and power electronics 158. In some embodiments, the user interface device 150 can also include one or more sensors, such as sensors described elsewhere herein. Furthermore, in some instances, the user interface device 150 can generate an alert responsive to device issues or a response to therapy. The alert may be received from the neurostimulation device 100.

In additional embodiments, data acquired from the one or more sensors 112 is processed by a combination of the hardware processor(s) 108 and hardware processor(s) 152. In further embodiments, data collected from one or more sensors 112 is transmitted to the user interface device 150 with little or no processing performed by the hardware processors 108. In some embodiments, the user interface device 150 can include a remote server that processes data and transmits signals back to the device 100 (e.g., via the cloud).

FIG. 2A illustrates a block diagram of an embodiment of a device and system 216 that provides peripheral nerve stimulation. In certain embodiments, the device and system 216 senses a biological measure, a kinematic measure, and/or user satisfaction data. In certain embodiments, the device and system 216 use the biological measure, the kinematic measure, and/or the user satisfaction data to customize or modify the delivery of an electrical stimulus.

In some embodiments, the system 216 comprises a pulse generator 200. In certain embodiments, the pulse generator 200 delivers electrical stimulation to a nerve through one or more skin interfaces 202. In certain embodiments, the one or more skin interfaces 202 can be an electrode 102. In certain embodiments, the one or more skin interfaces 202 sit adjacent to one or more target peripheral nerves. A controller 204 receive one on more signals generated by one or more sensors 206 to control timing and parameters of stimulation. In certain embodiments, the processor 204 uses instructions stored in the memory 208 to coordinate receiving signals from the one or more sensors 206. In certain embodiments, the processor 204 uses the received signal to control stimulation delivered by the pulse generator 200. The memory 208 in the system 216 can store signal data from the sensors 206.

In certain embodiments, the system 216 has a communication module 210 to transmit data to other devices or a remote server via standard wired or wireless communication protocols. In certain embodiments, the system 216 is powered by a battery 214. In certain embodiments, the system 216 has a user interface 212. In certain embodiments, the user interface 212 allows the user to receive feedback from the system 212. In certain embodiments, the user interface 212 allows the user to provide input to the system via, e.g., one or more buttons. In certain embodiments, the user provides satisfaction data via the user interface 212. For example, the user can provide input to the user interface 212 in the form of a patient session impression of improvement (PSII) score and/or a patient satisfaction scope. In certain embodiments, the user interface 212 allows a user to receive instructions, feedback, and control aspects of the delivered stimulation, such as intensity of the stimulation.

In certain embodiments, the controller 204 can receive kinematic and/or satisfaction data to determine a method for varying multiple stimulation parameters based on adaptive learning as disclosed herein. In certain embodiments, the controller 204 causes the device 100 to adjust one or more parameters of a first electrical stimulus based at least in part on the kinematic and/or satisfaction data.

FIG. 2B illustrates a block diagram of an embodiment of a controller 204 that can be implemented with the hardware components described with respect to FIGS. 1A, 1B, and 2A. The controller 204 can include multiple engines for performing the processes and functions described herein. The engines can include programmed instructions for performing processes as discussed herein for detection of input conditions, processing data, and control of output conditions. The engines can be executed by the one or more hardware processors of the neuromodulation (e.g., neurostimulation) device 100 alone or in combination with the base station 150, the user interface device 150, and/or the cloud. The programming instructions can be stored in the memory 208. The programming instructions can be implemented in C, C++, JAVA, or any other suitable programming languages. In some embodiments, some or all of the portions of the controller 204 including the engines can be implemented in application specific circuitry such as ASICs and FPGAs. Some aspects of the functionality of the controller 204 can be executed remotely on a server (not shown) over a network. While shown as separate engines, the functionality of the engines as discussed below is not necessarily required to be separated. Accordingly, the controller 204 can be implemented with the hardware components described above with respect to FIGS. 1A, 1B, and 2A.

The controller 204 can include a signal collection engine 216. The signal collection engine 216 can enable acquisition of raw/sensor data 218 from the sensors 112 embedded in the device 100 as well as user satisfaction data 220. The sensor data 218 can include but is not limited to accelerometer or gyroscope data from the IMU. In certain embodiments, the sensor data 218 can include test kinematic data taken during a therapy session. In certain embodiments, the sensor data 218 can include passive kinematic data. Passive kinematic data is data collected at times outside of the therapy session.

In some embodiments, the neuromodulation, e.g., neurostimulation device 100 or the user interface device 150 with sensors can collect kinematic or motion data (test and/or passive data), or data from other sensors, can measure data over a longer period of time, for example 1, 2, 3, 4, 5, 10, 20, 30 weeks, 1, 2, 3, 6, 9, 12 months, or 1, 2, 3, 5, 10 years or more or less, or ranges incorporating any two of the foregoing values, to determine features, or biomarkers, associated with the onset of tremor diseases, such as essential tremor, Parkinson's disease, dystonia, multiple sclerosis, Lyme disease, etc. Biomarkers could include specific changes in one or more features of the data over time, or one or more features crossing a predetermined threshold. In some embodiments, features of tremor inducing tasks have been stored on the neurostimulation device 100 and used to automatically activate sensors when those tremor inducing tasks are being performed, to measure and store data to memory during relevant times.

The devices, systems and methods described herein are used to treat Lyme disease (e.g., its associated symptoms) in some embodiments. The inflammation associated with Lyme disease is reduced in one embodiment (including for example, long term or chronic inflammation and/or flare ups). Resulting neurological conditions are treated in some embodiments, including but not limited to, weakness, numbness, nerve damage, and facial muscle paralysis. In addition to Lyme disease, chronic fatigue syndrome and its associate symptoms, such chronic inflammation, flare ups etc. are treated according to several embodiments. Treatment may be accomplished by, for example, vagal stimulation and/or sympathetic/parasympathetic balance. Systems and methods to reduce habituation and/or tolerance to nerve stimulation (such as vagus nerve stimulation via an earpiece) are provided in several embodiments by, for example, introducing variability in stimulation parameter(s), as described herein.

The satisfaction data 220 can include but is not limited to subjective data provided by the user. The subjective data can relate to pre or post treatment and/or patient activities of daily living (ADL). In certain embodiments, the user inputs a value that reflects a level of satisfaction. The level of satisfaction can be selected from a predetermined range. In certain embodiments, the range is from 1 to 4. Of course, the range can be any range and is not limited to 1 to 4. For example, the user can provide input to the user interface 212 in the form of a patient session impression of improvement (PSII) score and/or a user satisfaction score.

In some embodiments, the signal collection engine 216 can also perform signal preprocessing on the raw data. Signal preprocessing can include noise filtering, smoothing, averaging, and other signal preprocessing techniques to clean the raw data. In some embodiments, portions of the signals can be discarded by the signal collection engine 216. In some embodiments, portions of the signals are associated with a time stamp or other temporal indicator.

In certain embodiments, the controller 204 determines a level of patient therapeutic benefit based on the passive kinematic data from the sensor signals 218 without requiring the user to input a subjective satisfaction level. In certain embodiments, the controller 204 collects sensor signals 218 in the form of kinematic data measured during the therapy session along with satisfaction data 220 input by the user. In this way in certain embodiments, the controller 204 can determine a level of patient therapeutic benefit based on both the passive kinematic data and the patient provided subjective satisfaction level.

The controller 204 can further include a learning algorithm 222. In certain embodiments, the learning algorithm 222 selects from methods for varying parameter(s) employed during therapy session based on adaptive learning to improve tremor therapeutic treatment 224.

In certain embodiments, the learning algorithm 222 can select from a plurality of stimulation parameters (e.g., burst frequency and pulse frequency) to vary one parameter across one or more nerves (e.g., median and/or radial nerve) and/or select multiple stimulation parameters to vary across one or more nerves.

In certain embodiments, the plurality of stimulation parameters accessed by the learning algorithm 222 can be a subset of all of the stimulation parameters and or patterns of applying stimulation parameters. For example, in certain embodiments, the learning algorithm 222 selects the response profile(s) for which a positive outcome is predicted by the learning algorithm 222. In certain embodiments, the learning algorithm 222 modifies the one or more parameters of the selected stimulation parameters based on the individual user to further personalize the stimulation parameters and improve neurostimulation therapy outcomes.

The learning algorithm 222 can automatically determine a correlation between the satisfaction data 220 and/or the sensor signals 218 and neurostimulation therapy outcomes. Outcomes can include, for example, identifying patients who will respond to the therapy (e.g., during an initial trial fitting or calibration process) based on tremor features of kinematic data from the sensor signals 218 (e.g., approximate entropy), predicting stimulation settings for a given patient (based on their tremor features) that will result in the best therapeutic effect (e.g., dose, where parameters of the dose or dosing of treatment include but are not limited to duration of stimulation, frequency and/or amplitude of the stimulation waveform, and time of day stimulation is applied), predicting patient tremor severity at a given point, predicting patient response over time, examining patient medication responsiveness combined with tremor severity over time, predicting response to transcutaneous or percutaneous stimulation, or implantable deep brain stimulation or thalamotomy based off of tremor features and severity over time, and predicting ideal time for a patient to receive transcutaneous or percutaneous stimulation, or deep brain stimulation or thalamotomy based off of tremor features and severity over time, predicting patient reported therapy outcomes or patient reported satisfaction using tremor features assessed kinematic measurements from the device; predicting patient response to undesirable user experience using tremor features assessed from kinematic measurements and patient usage logs from the device where undesirable user experiences can include but are not limited to device malfunctions and adverse events such as skin irritation or bum; predict patient response trends based on tremor severity where trends can be assessed across total number of sessions, within an individual patient, or across a population of patients; predicting or classifying subtypes of tremor to predicting patient response based on kinematic analysis of tremor features; predicting or classifying subtypes of tremor to provide guidance for individually optimized therapy parameters; predicting or classifying subtypes of tremor to optimize the future study design based on subtypes (e.g., selecting specific subtypes of essential tremor for a clinical study with specific design addressing therapy need for the subtype); and predict patient or customer satisfaction (e.g., net promoter score) based on patient response or other kinematic features from measure tremor motion.

In some embodiments, the neuromodulation, e.g., neurostimulation device 100 or the user interface device 150 with sensors 218 can collect kinematic or motion data, or data from other sensors, when a tremor inducing task is being performed. The user can be directly instructed to perform the task, for example via the display 116 on the device 100 or audio. In some embodiments, features of tremor inducing tasks are stored on the device 100 and used to automatically activate sensors to measure and store data to memory during relevant tremor tasks. The period of time for measuring and storing data can be, for example, 1-180 seconds such as 10, 20, 30, 60, 90, 120 seconds, or 1-60 minutes such as 1, 2, 3, 5, 10, 15, 20, 30 minutes, or 1-12 hours such as 1, 2, 3, 4, 5, 6, 7, 8 hours or more or less, or ranges incorporating any two of the foregoing values. Based on a training set of data from a cohort of previous wearers with tremor or another condition, the learning algorithm 222 can detect features that are correlated with response to stimulation such that the patient or physician can be presented with one or more response profiles. The one or more response profiles can correspond to neurostimulation therapy that has a qualitative likelihood for patient response.

In another embodiment, features can be correlated with the type of tremor measured, such as essential tremor, resting tremor (associated with Parkinson's Disease), postural tremor, action tremor, intention tremor, rhythmic tremor (e.g., a single dominant frequency) or mixed tremor (e.g., multiple frequencies). In some embodiments, essential tremor pathology can include, for example, a primarily cerebellar variant with Bergmann gliosis and Purkinje cell torpedoes, and a Lewy body variant, and a dystonic variant, and a multiple sclerosis variant, and a Parkinson disease variant. The type of tremor most likely detected can be presented to the patient or physician as a diagnosis or informative assessment prior to receiving stimulation or to assess appropriateness of prescribing a neuromodulation, e.g., stimulation treatment. In another embodiment, various response profiles may be applied based on the tremor type determined; different profiles could include changes in stimulation parameters, such as frequency, pulse width, amplitude, burst frequency, duration of stimulation, or time of day stimulation is applied. In one embodiment, the user interface device 150 can include an app that asks the patient to take a self-photograph or self-video, which has the patient perform a task that has both posture and intention actions.

In some embodiments, the neuromodulation, e.g., neurostimulation device 100 can apply transcutaneous stimulation to a patient with tremor that is a candidate for implantable deep brain stimulation or thalamotomy. Tremor features and other sensor measurements of tremor severity will be used to assess response over a prespecified usage period, which could be 1 month or 3 months, or 5, 7,14, 30, 60, or 90 days or more or less. The response to transcutaneous stimulation as assessed, for example, by the learning algorithm 222 described herein using sensor measurements from the device and/or patient satisfaction data can advantageously provide an assessment of the patient's likelihood to respond to implantable deep brain stimulation or other implantable or non-implantable therapies.

In some embodiments, the learning algorithm 222 develops rules between the satisfaction data 220 and/or sensor signals 218 and one or more parameters of one or more response profiles that correspond to neurostimulation therapy outcomes. The learning algorithm 222 can employ machine learning modeling along with signal processing techniques to determine rules, where machine learning modeling and signal processing techniques include but are not limited to: supervised and unsupervised algorithms for regression and classification. Specific classes of algorithms include, for example, Artificial Neural Networks (Perceptron, Back-Propagation, Convolutional Neural Networks, Recurrent Neural networks, Long Short-Term Memory Networks, Deep Belief Networks), Bayesian (Naive Bayes, Multinomial Bayes and Bayesian Networks), clustering (k-means, Expectation Maximization and Hierarchical Clustering), ensemble methods (Classification and Regression Tree variants and Boosting), instance-based (k-Nearest Neighbor, Self-Organizing Maps and Support Vector Machines), regularization (Elastic Net, Ridge Regression and Least Absolute Shrinkage Selection Operator), and dimensionality reduction (Principal Component Analysis variants, Multidimensional Scaling, Discriminant Analysis variants and Factor Analysis). In some embodiments, the controller 204 can use the rules developed between features and one or more parameters to automatically determine response profiles that correspond to neurostimulation therapy outcomes. The controller 204 can also use the one or more response profiles to control or change settings of the neurostimulation device, including but not limited to stimulation parameters (e.g., stimulation amplitude, frequency, patterned (e.g., burst stimulation), intervals, time of day, individual session or cumulative on time, and the like).

Accordingly, the one or more response profiles that correspond to neurostimulation therapy can improve operation of the neuromodulation, e.g., neurostimulation device, and advantageously and accurately identify potential candidates for therapy and well as various disease state and therapy parameters over time. The generated one or more response profiles that correspond to neurostimulation therapy can be saved in the memory 110 and/or memory 208. For example, the methods for varying one or more stimulation parameters can be generated and stored prior to operation of the neurostimulation device 100. Accordingly, in some embodiments, the controller 204 can apply the saved one or more profiles based on new data collected by the sensors 112, 206 to determine outcomes or control the neuromodulation, e.g., neurostimulation device 100.

FIG. 2C schematically illustrates an embodiment of a neuromodulation device 100 and base station 120. The neurostimulation device 100 can include a stimulator 103 and detachable band 101 including two or more working electrodes 102 (positioned over the median and radial nerves) and a counter-electrode positioned on the dorsal side of the wrist. The electrodes 102 could be, for example, dry electrodes or hydrogel electrodes. The base station 120 can be configured to stream movement sensor and usage data on a periodic basis, e.g., daily and charge the neurostimulation device 100. The device stimulation bursting frequency can be calibrated to a lateral postural hold task “wing-beating” or forward postural hold task for a predetermined time, e.g., 5-30 seconds (e.g., 20 seconds) for each subject Other non-limiting examples of device parameters can be as disclosed elsewhere herein.

In some embodiments, stimulation may alternate between each nerve such that the nerves are not stimulated simultaneously. In some embodiments, all nerves are stimulated simultaneously. In some embodiments, stimulation is delivered to the various nerves in one of many bursting patterns. The stimulation parameters may include on/off, time duration, intensity, pulse rate, pulse width, waveform shape, and the ramp of pulse on and off. In one embodiment the stimulation may last for approximately 10 minutes to 1 hour, such as approximately 10, 20, 30, 40, 50, or 60 minutes, or ranges including any two of the foregoing values.

In some embodiments, a plurality of electrical stimuli can be delivered offset in time from each other by a predetermined fraction of multiple of a period of a measured rhythmic biological signal such as hand tremor, such as about %, %, or % of the period of the measured signal for example. Further possible stimulation parameters are described, for example, in U.S. Pat. No. 9,452,287 to Rosenbluth et al., U.S. Pat. No. 9,802,041 to Wong et al., PCT Pub. No. WO 2016/201366 to Wong et al., PCT Pub. No. WO 2017/132067 to Wong et al., PCT Pub. No. WO 2017/023864 to Hamner et al., PCT Pub. No. WO 2017/053847 to Hamner et al., PCT Pub. No. WO 2018M09680 to Wong et al., PCT Pub. No. WO 2018/039458 to Rosenbluth et al., and PCT Pub. No. WO 2020/086726 to Hamner et al., each of the foregoing of which are hereby incorporated by reference in their entireties.

In some embodiments, a neuromodulation device can include the ability to track a user's motion data for the purpose of gauging one, two, or more tremor frequencies of a patient. The patient could have a single tremor frequency, or in some cases multiple discrete tremor frequencies that manifest when performing different tasks. Once the tremor frequencies are observed, they can be used as one of many seminal input parameters to a customized neuromodulation therapy. The therapy can be delivered, e.g., transcutaneously, via one, two, three or more nerves (e.g., the median and radial nerves, and/or other nerves disclosed elsewhere herein) in order to reduce or improve a condition of the patient, including but not limited to their tremor burden. In some embodiments, the therapy modulates afferent nerves, but not efferent nerves. In some embodiments, the therapy preferentially modulates afferent nerves. In some embodiments, the therapy does not involve functional electrical stimulation. The tremor frequency can be used to calibrate the patients neuromodulation therapy, being used as a calibration frequency in some embodiments to set one or more parameters of the neuromodulation therapy, e.g., a burst envelope period. In some embodiments, the calibration frequency can be between, for example, about 4 Hz and about 12 Hz, between about 3 Hz and about 6 Hz, or about 3 Hz, 4 Hz, 5 Hz, 6 Hz, 7 Hz, 8 Hz, 9 Hz, 10 Hz, 11 Hz, or 12 Hz, or ranges including any two of the foregoing values.

In some embodiments, stimulation may be applied to two or more nerves in an alternating manner at an interval defined by the tremor frequency (also referred to as burst frequency). In some embodiments, burst frequency is equal to the measured pathological tremor oscillation, which calculated from measured motion, muscle activity, or brain activity.

Various embodiments of the devices and/or systems discussed herein can stimulate nerves in an outer ear of a user, including but not limited to the auricular branch of the vagus nerve, great auricular nerve, auriculotemporal nerve, and/or lesser occipital nerve, among others. In one embodiment, a system can include a neuromodulation device on the wrist or other location of the arm to target a nerve of a subject (e.g., median nerve) and a neuromodulation device (such as any of the auricular devices described herein) in the ear to target the vagus nerve. In some implementations, each neuromodulation device in the system can communicate with each other via a wired or wireless connection. Multiple neuromodulation devices can provide synchronized stimulation to the multiple nerves. Stimulation may be, for example, burst, offset, or alternating between the multiple nerves. Modulation of the vagus nerve can be accomplished with the devices described herein, according to several embodiments. In some embodiments, the devices described herein are used to stimulate the autonomic system. In some embodiments, the devices described herein are used to balance the sympathetic/parasympathetic systems.

Not to be limited by theory, variability of stimulation parameters, including but not limited to jitter or dither-like variability, can enhance the symptomatic and/or long-term reduction of tremor severity provided by the application of alternating stimulation between two or more peripheral nerves. This approach can overcome the challenge of variability observed in people with hand tremor between tremor episodes within an individual, or the variability observed between people in their brain response to peripheral nerve stimulation. Thus, several embodiments include systems and methods to reduce habituation and/or tolerance to stimulation by, for example, introducing variability in stimulation parameter(s).

Adding variation in burst frequency may account for natural variation in pathological tremor frequency. For example, in some cases pathological tremor frequency can change, for example, by more than 2 Hz between tasks and by up to 32% on the same task over time within an individual subject. Calibrating burst frequency to tremor frequency can improve therapeutic effect. However, as discussed above, it may be difficult to target particular tremor frequencies due to the natural variations. In some instances, it may not be suitable to continuously track the changing tremor characteristics using sensors discussed herein. It may consume too many computational resources and may also deplete battery. Therefore, the inventors realized that instead of focusing on a particular value or trying to exactly align to a pathological characteristic, adding variation in stimulation parameters, such as burst frequency, may enhance therapeutic benefit in treatment of conditions. Pathological characteristics can vary depending on the pathological condition. For example, for treatment of tremor, the characteristics of tremor may include tremor frequency, power, phase, amplitude, and the like. For example, for treatment of migraine, a 3 Hz burst frequency with a 150 Hz pulse frequency may override thalamocortical dysrhythrria in individuals. For example, for treatment of stroke, a 1 Hz burst frequency with a 10 Hz pulse frequency may reduce neuronal inhibition in the motor cortex that otherwise inhibits motor activity in individuals. In some instances, the characteristics may include physiological parameters, such as heart rate, respiration rate, heart rate variability, blood pressure, and the like. The characteristics may also correspond to sympathetic and/or parasympathetic activity. Furthermore, the characteristics may correspond to neural oscillations. In some instances, neural oscillations may be observed in alpha, beta, delta, theta, gamma frequency bands. In some embodiments, EEG sensor is not required to probe these oscillations and provide therapeutic effect based on stimulation.

In some instances, variations will increase probability of alignment with the changing pathological characteristics during a portion of the therapy session, over time and across tasks. In some embodiments, one or more stimulation parameters are continuously varied over the course of the stimulation. Furthermore, in some instances, measuring tremor characteristics with one or more sensors is not required to provide a therapeutic effect. In addition to tremor, introduction of variability to treat conditions other than tremor are also provided (e.g., other movement disorders, migraine, stroke, other neurological disorders, etc.).

In additional embodiments, stimulation parameters are agnostic for any particular individual and may be varied within generally known therapeutic ranges during the course of stimulation. Adding variation in pulse frequency may account for individual differences in the brain response to peripheral nerve stimulation. For example, the evoked response generated in the ventral intermediate nucleus of the thalamus by median nerve stimulation was maximized at a pulse frequency of 50 Hz in some subjects and 100 Hz in other subjects. By varying pulse frequency throughout these range of values, the brain response is maximized during some portion of the therapy session for every individual, which may enhance therapeutic benefit. Varying pulse frequency during deep brain stimulation (DBS) therapy improved motor score outcomes, gait speed, and freezing of gait episodes in Parkinson's disease patients, compared to fixed frequency DBS. Finally, varying pulse frequency may produce natural stimulation-evoked sensations.

Adding variation in pulse intensity, current amplitude, voltage amplitude, or pulse width would be expected to change the extent of neuronal recruitment within the targeted nerves, with higher intensities and amplitudes, or longer pulse widths, increasing the extent of recruitment. These variations in nerve recruitment may vary the degree of activation in downstream neuronal sub-populations within the brain, which in turn could enhance therapeutic benefit, potentially by reducing the likelihood of neuronal adaptation or habituation to stimulation. In addition, varying pulse intensity or pulse width may produce more natural stimulation-evoked sensations than fixed stimulation. Systems and methods to reduce habituation and/or tolerance to stimulation are provided in several embodiments by, for example, introducing variability in stimulation parameter(s), as described herein. Habituation and/or tolerance to neurostimulation that occur in the treatment of movement, inflammatory, neurological and psychiatric disorders are treated in several embodiments.

Adding on/off periods in the stimulation waveform may enhance the therapeutic effects by increasing the desired desynchronization effect in downstream neuronal sub-populations within the brain.

Additionally, not to be limited by theory, variability in any of the above parameters can enhance the desired neuronal desynchronization effect that enhances therapeutic benefit (e.g., a lower tremor or symptom severity after application of stimulation).

Variability can be applied to one or more of the following parameters for stimulating a nerve including but not limited to burst frequency or alternating frequency, pulse frequency, pulse width, pulse spacing, intensity, current amplitude, voltage amplitude, duration of stimulation, on/off periods, or amplitude envelope periods. Variability can be applied across multiple stimulation parameters for stimulating a nerve including but not limited to simultaneous variation, braided variation, timescale variation, and adaptive learning. In certain embodiments, adaptive learning is employed in combination with the listed variations as well as other variations to improve neurostimulation therapy outcomes.

FIG. 3A illustrates an embodiment of the device 100 that delivers patterned stimulation to the median nerve 302 and radial nerve 304 where burst frequency is varied after a prespecified time period or prespecified number of bursts. As is illustrated in Plot 3A, the burst frequency is initially burst frequency A with a period of 1/f₁306. The burst frequency subsequently changes to burst frequency B with a different period of 1/f₂308. Plot 3A is only exemplary and is not intended to limit the variations in burst frequency to the illustrated values or the number of different burst frequencies. Further, while FIG. 3A illustrates the variation occurring across multiple nerves (e.g., median and radial nerves), the disclosure is not so limited. The disclosed variations can be applied to only a single nerve.

In some embodiments, burst frequency variability is centered on an about, at least about, or no more than about 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, or 6 Hz or more or less window (or ranges including any two of the foregoing values), or any combination thereof, around a calibration frequency measured from a tremor-inducing task, such as a postural hold. In certain embodiments, if the measured tremor frequency is at a lower edge of a partial tremor frequency range (e.g., a 3-12 Hz window), the burst frequency variation window would not go below 3 Hz. In certain embodiments, if the measured tremor frequency is at the higher edge of a partial tremor frequency range (e.g., a 3-12 Hz window), the burst frequency variation window would not go above 12 Hz. In an alternative embodiment, burst frequency variability is applied within the full or partial tremor frequency range, for example between 3-12 Hz for essential tremor. This alternative embodiment may have the advantage of not requiring the user to perform a tremor inducing task for calibration. In yet another embodiment, the range of values for burst frequency variability is set based on the minimum and maximum tremor frequencies measured from multiple tremor-inducing task measurements. Not to be limited by theory, burst frequency variability can avoid exact alignment to the pathological oscillation frequency over time and enhance the therapeutic response compared to a constant burst frequency. In some embodiments, the rate of change of the burst frequency parameter may be between 0.001 Hz/s (i.e., slowest rate of change of burst frequency being in increments of 0.1 Hz every 100 sec) to 100 Hz/s (i.e., fastest rate of change of burst frequency being in increments of 8 Hz burst frequency change every tremor cycle, and rounding up).

FIG. 3B illustrates an embodiment of the device 100 that delivers patterned stimulation to the median nerve 302 and radial nerve 304 where pulse frequency is varied after a prespecified time period or prespecified number of bursts. As is illustrated in FIG. 38, the pulse frequency is initially pulse frequency A with a period of 1/F₁310. The pulse frequency subsequently changes to pulse frequency B with a different period of 1/F₂312. FIG. 3B is only exemplary and is not intended to limit the variations in pulse frequency to the illustrated values or number of pulse frequencies. Further, while FIG. 3B illustrates the variation occurring across multiple nerves (e.g., median and radial nerves), the disclosure is not so limited. The disclosed variations can be applied to only a single nerve.

Not be limited by theory, the pulse frequency of electrical stimulation applied to a peripheral nerve or neuron can govern how frequently the stimulated nerve or neuron generates an action potential. In some cases, peripheral nerve fibers can be activated to generate an action potential with every stimulation pulse at pulse frequencies of less than approximately 1,000 Hz, if the stimulation pulse width and amplitude are sufficiently high. In some cases, stimulation of the median nerve with pulse frequencies of 5, 50, 100, 150, and 200 Hz can evoke a response of the VIM thalamus, as measured with implanted microelectrodes during a surgical procedure. Moreover, the pulse frequency that generates the maximal amplitude evoked response of the VIM thalamus can vary across subjects. In some embodiments, pulse frequency is varied between 5-200, 5-150, 5-100, 5-50, 50-200, 50-150, 50-100, 100-200, 100-150, or 150-200 Hz (or ranges including any two of the foregoing values), which can enhance therapeutic response compared to a constant pulse frequency. Changes in pulse frequency may be implemented by changing the timing of pulse delivery directly, or by keeping the timing fixed and alternating stimulation amplitude on a pulse-to-pulse basis to change the effective pulse frequency. For example, setting every 1 of 2 pulses to a low stimulation amplitude, which is subthreshold for recruitment of neurons or nerves, can reduce the effective pulse frequency by M. In some embodiments, the rate of change of the pulse frequency parameter may be between 0.001-10,000 Hz/s. Not to be limited by theory, varying pulse frequency may generate activity in the brain that modulates pathological cortical dynamics associated with hand tremor. An additional advantage of varying pulse frequency is that this type of stimulation can elicit a more natural paresthesia sensations, similar to tapping, pressure, touch, and/or vibration sensations experienced during daily life.

In one embodiment the pulse frequency may be from about 1 to about 5000 Hz, about 1 Hz to about 500 Hz, about 5 Hz to about 50 Hz, about 50 Hz to about 300 Hz, or about 150 Hz, or other ranges including any two of the foregoing values. In some embodiments, the pulse frequency may be from 1 kHz to 20 kHz.

FIG. 4A illustrates an embodiment of the device 100 that delivers patterned stimulation to the median nerve 302 and radial nerve 304 where current amplitude is varied after a prespecified time period or prespecified number of bursts. As is illustrated in Plot 4A, the current amplitude is initially current amplitude A with a value 402. The current amplitude subsequently changes to current amplitude B with a different value 404. In the illustrated embodiment, the value 404 is greater than the value 402 by an amount 406. FIG. 4A is only exemplary and is not intended to limit the variations in current amplitude to the illustrated values or number of different amplitudes. Further, while FIG. 4A illustrates the variation occurring across multiple nerves (e.g., median and radial nerves), the disclosure is not so limited. The disclosed variations can be applied to only a single nerve.

The intensity of the electrical stimulation may vary from 0 mA to 500 mA, and a current may be approximately 1 to 11 mA in some cases. The electrical stimulation can be adjusted in different patients and with different methods of electrical stimulation. The increment of intensity adjustment may be, for example, 0.1 mA to 1.0 mA.

FIG. 48 illustrates an embodiment of the device 100 that delivers patterned stimulation to the median nerve 302 and radial nerve 304 where pulse width is varied after a prespecified time period or prespecified number of bursts. As is illustrated in FIG. 4B, the pulse width is initially pulse width A with a value 408. The pulse width subsequently changes to pulse width B with a different value 410. As is illustrated by comparison 412, the value 410 is greater than the value 408. Of course, the subsequent value 410 could be less than value 412 in other embodiments. FIG. 48 is only exemplary and is not intended to limit the variations in pulse width to the illustrated values or number of different pulses. Further, while FIG. 4B illustrates the variation occurring across multiple nerves (e.g., median and radial nerves), the disclosure is not so limited. The disclosed variations can be applied to only a single nerve. A pulse width may range from, in some cases, 50 to 500 μs (micro-seconds), such as approximately 300 μs.

Not to be limited by theory, the pulse width of electrical stimulation applied to a peripheral nerve or neuron can be one factor that determines the number and types of nerves or neurons activated with each stimulation pulse. More specifically, varying pulse width applied to a peripheral nerve could advantageously produce a more pronounced desynchronization effect in activated brain region, including but not limited to thalamus, as this can vary the size of the neuronal sub-populations that are recruited during peripheral nerve stimulation. For example, an electrical stimulation pulse train with a fixed pulse width will recruit the same set of neurons, nerves, or nerve fibers with each pulse, which is not a natural characteristic of neuronal activity. In contrast, natural stimuli to the nervous system generate action potentials in a more probabilistic and stochastic fashion. Not to be limited by theory, varying stimulation pulse width over time could be used to activate distinct neuronal populations with each pulse, which could more closely resemble physiological neural signaling. Varying pulse width can produce more natural sensations with stimulation of the median, radial, and ulnar nerves using implanted nerve cuffs in patients with upper limb amputation, and equally or more comfortable sensations with spinal cord stimulation for treatment of neuropathic pain. In some embodiments, pulse width could be varied between sensory threshold and maximum comfortable threshold for an individual, with stimulation amplitude (also referred to as current level or voltage level) held constant. Pulse width of transcutaneously applied electrical stimulation affects comfort and perceived sensation, so ranges can be determined based on feedback of an individual user.

In an alternative embodiment, the pulse width can be varied between a minimum and maximum set for each individual, where the minimum value is, for example, from about, at least about, or no more than about one of 100, 150, 200, 250, 300, or 350 microseconds and the maximum value is set based on an individual's comfort level at a fixed stimulation amplitude, and the rate of variation is restricted to (e.g., consists essentially of or comprises) 0.01-10,000 microseconds per second. In a further embodiment, the fixed stimulation amplitude is based on an individual's sensory level with a fixed pulse width in a range, for example, of between 100-500 microseconds (e.g., 100-250 microseconds, 250-500 microseconds, and overlapping ranges therein).

In alternative embodiments, stimulation amplitude is varied while pulse width is kept constant. Not to be limited by theory, variation of stimulation amplitude (also referred to as current or voltage level, or current or voltage amplitude) can activate distinct neuronal populations with each pulse. In a further embodiment, the range of stimulation amplitude variation is restricted to (e.g., consists essentially of or comprises) a minimum set to the stimulation amplitude at an individual's minimum sensory threshold and a maximum set to the stimulation amplitude at an individual's maximum comfort level. In another embodiment, the minimum is set to a stimulation amplitude at a pre-specified increment below an individual's minimum sensory threshold (sub-sensory) and a maximum set to the stimulation amplitude at an individual's maximum comfort level wherein the pre-specified increment is, for example, about, at least about, or no more than about one of 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.9 or 1 mA. In some embodiments, the rate of change of the stimulation amplitude parameter may be between 0.001-10 mA/s.

FIGS. 5A-B illustrate multiple examples of stimulation patterns with prespecified on/off periods as stimulation is alternated across two nerves (e.g., median and radial nerve). The plots show patterns of current delivered by the device 100 over time. FIG. 5A illustrates an embodiment of the device 100 that delivers patterned stimulation to the median and radial nerves where stimulation is delivered for three bursts 502 (i.e., on period) and stimulation is not delivered for two bursts 504 (i.e., off period). In some embodiments, bursts can be defined by the period of the user's measured hand tremor as measured by motion sensors onboard the device 100. In certain embodiments, the period of the user's measured hand tremor corresponds to an initial burst pattern applied by the device 100. In certain embodiments, the device 100 subsequently varies the initial burst as shown in, for example, FIG. 58. FIG. 58 illustrates a similar embodiment where burst frequency is varied between two or more prespecified values across on periods. For example, the device 100 in FIG. 58 delivers patterned stimulation to the median and radial nerves where stimulation is delivered at a burst frequency A having a period of 1/f₁506 (i.e., on period) followed by an off period 510. The device 100 then delivers stimulation at a burst frequency B having a period of 1/f₂508.

FIG. 6A illustrates an example of a ramping variation of the burst frequency parameter 602 over time 604. The burst frequency linearly ramps 600 from 3 Hz to 12 Hz in time period of 2 seconds, which results in a rate of change of 4.5 Hz/s. FIG. 6B illustrates another example of a ramping variation of the burst frequency parameter 602 over time 604. The burst frequency linearly ramps 606 from 3 Hz to 3.4 Hz in time period of 5 seconds, which results in a rate of change of 0.08 Hz/s. In some embodiments, one or more stimulation parameters could be varied as stimulation is applied to one or more target nerves or neurons, where stimulation parameters include burst frequency, pulse frequency, pulse width, on/off cycling, and stimulation amplitude. In some embodiments, variation can be performed as a sweep across a prespecified range of parameters (e.g., a linear ramp of values, an example of which is shown in FIGS. 6A and 6B, or sinusoidally-varying values).

In certain embodiments, a randomized or pseudo-randomized variation of parameters can be applied across a prespecified range of parameter values. In a further embodiment, variation of parameters can be distributed based on a predefined probabilistic distribution, including but not limited to a uniform, normal, Gaussian, chi square, binomial, or Poisson distribution. Alternatively, the probabilistic distribution function used to select the values for variation of parameters, such as burst frequency, can be set based on the observed tremor frequency distribution from multiple tremor-inducing task measurements. In some embodiments, there is a prespecified rate of changing parameters, which could theoretically range from changing a parameter value on a prespecified number of stimulation cycles, including but not limited to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more or less tremor cycles, or ranges including any two of the foregoing values (e.g., defined by burst or pulse frequency, non-limiting examples of which are shown in FIG. 3A and FIG. 3B, where the parameter value changes after every 3 stimulation cycles) up to the order of minutes. In some embodiments, this rate of parameter variation is selectable by the end user from a prespecified list of options. In other embodiments, the rate of parameter variation is set by the learning algorithm 222 based on some measured tremor characteristic, such as the rate of change in tremor frequency over time. The change in parameter values may occur instantaneously, or after a period in which stimulation is temporarily turned off for a duration between, for example, approximately 0.1 second and 10 seconds, as illustrated in FIGS. 5A and 58.

FIG. 7 illustrate an example of how multiple stimulation parameters (e.g., parameters A 702 and B 704) are simultaneously varied between two or more prespecified values as stimulation is applied to a nerve (e.g., median or radial nerve). As is illustrated in FIG. 7, parameter A 702 has a value 1 706 followed by a value 2 708. Parameter B 704 has a value 1 710 followed by a value 2 712. Parameter A 702 and parameter B 704 both change to their respective values 2 simultaneously. The values of parameter A 702 and parameter B 704 both further change to their respective values 3 simultaneously. Of course, the illustrated embodiment and values are only exemplary. In other embodiments, three or more stimulation patterns are simultaneously varied.

In certain embodiments, the method of varying multiple stimulation parameters in FIG. 7 is applied to at least one nerve. In other embodiments, the method for varying stimulation parameters in FIG. 7 is applied to multiple nerves. For example, parameters A 702 and B 704 can be varied for a first nerve (e.g., median nerve) accordingly to the method illustrated in FIG. 7 and for a second nerve (e.g., radial nerve) according to the method illustrated in FIG. 7. Of course, the values of the parameters for the first nerve need not be the same as the values of the parameters for the second nerve.

In certain embodiments, the same parameters (e.g., parameters A and B) are varied across at least two nerves. In other embodiments, the parameters varied across the first nerve according to the method illustrated in FIG. 7 are different from the parameters varied across the second nerve according to the method illustrated in FIG. 7.

In certain embodiments, the method of FIG. 7 can be implemented by alternating stimulation between multiple nerves with a specific burst frequency or used to stimulate a single nerve. In certain embodiments where multiple nerves are stimulated, the stimulation parameters can be varied for stimulation of the first nerve but may be fixed for stimulation of the second nerve.

In certain embodiments, the parameter values 706-712 disclosed in FIG. 7 can change over time. For example, in certain embodiments, the parameter values 706-712 change from therapy session to therapy session. As further explained below in certain embodiments, the parameter values 706-712 can be changed based on the learning algorithm 222 to optimize therapy. In certain embodiments, the parameter values are changed based on pre-session measures, such as tremor kinematic characteristics or system impedance.

FIG. 8 illustrate an example of how multiple stimulation parameters (e.g., parameters A 802 and B 804) are varied by alternately changing each parameter between two or more prespecified values as stimulation is applied to a nerve (e.g., median or radial nerve). As is illustrated in FIG. 8, parameter A 802 has a value 1 806 followed by a value 2 808. Parameter B 804 has a value 1 810 followed by a value 2 812. Parameter A 802 and parameter B 804 alternate changing their respective values. The values of parameter A 802 and parameter B 804 both alternate changing to their respective values 3. In certain embodiment, the values of parameter A 802 and parameter B 804 change asynchronously. Of course, the illustrated embodiment and values are only exemplary. In other embodiments, three or more stimulation patterns are alternately varied.

In certain embodiments, the method of varying multiple stimulation parameters in FIG. 8 is applied to at least one nerve. In other embodiments, the method for varying stimulation parameters in FIG. 8 is applied to multiple nerves. For example, parameters A 802 and B 804 can be varied for a first nerve (e.g., median nerve) accordingly to the method illustrated in FIG. 8 and for a second nerve (e.g., radial nerve) according to the method illustrated in FIG. 8. Of course, the values of the parameters for the first nerve need not be the same as the values of the parameters for the second nerve.

In certain embodiments, the same parameters (e.g., parameters A and B) are varied across at least two nerves. In other embodiments, the parameters varied across the first nerve according to the method illustrated in FIG. 8 are different from the parameters varied across the second nerve according to the method illustrated in FIG. 8.

In certain embodiments, the method of FIG. 8 can be implemented by alternating stimulation between multiple nerves with a specific burst frequency or used to stimulate a single nerve. In certain embodiments where multiple nerves are stimulated, the stimulation parameters can be varied for stimulation of the first nerve but may be fixed for stimulation of the second nerve.

In certain embodiments, the parameter values 806-812 disclosed in FIG. 8 can change over time. For example, in certain embodiments, the parameter values 806-812 change from therapy session to therapy session. As further explained below in certain embodiments, the parameter values 806-812 can be changed based on the learning algorithm 222 to optimize therapy. In certain embodiments, the parameter values are changed based on pre-session measures, such as tremor kinematic characteristics or system impedance.

FIG. 9 illustrate an example of how multiple stimulation parameters (e.g., parameters A and B) are varied by applying different timescales to each parameter as stimulation is applied to a nerve (e.g., median or radial nerve). As is illustrated in FIG. 9, parameter A 902 has a value 1 906 followed by a value 2 908. Parameter B 904 has a value 1 910 followed by a value 2 912. Parameter A 902 and parameter B 904 change their respective values based on different timescales. The values of parameter A 902 and parameter B 904 both change based on their respective timescale. Of course, the illustrated embodiment and values are only exemplary. In other embodiments, three or more stimulation patterns are alternately varied.

In certain embodiments, parameter A 902 (e.g., stimulation amplitude, pulse width) and parameter B 904 (e.g., burst frequency, pulse frequency) are varied on different timescales. For example, in certain embodiments, parameter A 902 may be varied pulse-to-pulse (every few tens of milliseconds or hundreds of milliseconds), whereas parameter B 904 may be varied on a time scale of seconds to minutes.

In certain embodiments, the method of varying multiple stimulation parameters in FIG. 9 is applied to at least one nerve. In other embodiments, the method for varying stimulation parameters in FIG. 9 is applied to multiple nerves. For example, parameters A 902 and B 904 can be varied for a first nerve (e.g., median nerve) accordingly to the method illustrated in FIG. 9 and for a second nerve (e.g., radial nerve) according to the method illustrated in FIG. 9. Of course, the values of the parameters for the first nerve need not be the same as the values of the parameters for the second nerve.

In certain embodiments, the same parameters (e.g., parameters A and B) are varied across at least two nerves. In other embodiments, the parameters varied across the first nerve according to the method illustrated in FIG. 9 are different from the parameters varied across the second nerve according to the method illustrated in FIG. 9.

In certain embodiments, the method of FIG. 9 can be implemented with different timescales for multiple nerves with a specific burst frequency or used to stimulate a single nerve. In certain embodiments where multiple nerves are stimulated, the stimulation parameters can be varied for stimulation of the first nerve but may be fixed for stimulation of the second nerve.

In certain embodiments, the parameter values 906-912 disclosed in FIG. 9 can change over time. For example, in certain embodiments, the parameter values 906-912 change from therapy session to therapy session. As further explained below in certain embodiments, the parameter values 906-912 can be changed based on the learning algorithm 222 to optimize therapy. In certain embodiments, the parameter values are changed based on pre-session measures, such as tremor kinematic characteristics or system impedance.

In certain embodiments, different methods (e.g., methods disclosed in FIGS. 7-9) for varying multiple parameters can be used for different therapy session or during the same therapy session. For example, in certain embodiments, simultaneous variation of parameters as disclosed in FIG. 7 can be used for a first time frame (e.g., 5 minutes) of the therapy session, followed by a braided variation (FIG. 8) for a second time frame (e.g., next 5 minutes) of the therapy session. In certain embodiments, the values or first set of parameters varied during a first time frame are followed by a second set of parameters which are varied during a second time frame.

In certain embodiments, adaptive learning via the learning algorithm 222 is employed in combination with any of the methods illustrated in FIG. 7-9. In certain embodiments, the learning algorithm 222 uses active and/or passive kinematic measurements during or after stimulation sessions to assess how stimulation parameter changes impact real-time therapeutic outcomes (e.g., tremor improvements). For example, if specific parameter values produce greater therapeutic outcomes than other values, then the stimulation method is modified during the same session to only use the corresponding parameter values.

In certain embodiments, the learning algorithm 222 uses satisfaction data during or after stimulation sessions to assess how stimulation parameter changes impact real-time therapeutic outcomes (e.g., tremor improvements). For example, if specific parameter values produce greater therapeutic outcomes than other values, then the stimulation method is modified during the same session to only use the corresponding parameter values.

FIG. 10 illustrates a flow chart of an embodiment of a process 1000 for varying one or more parameters of a stimulus over a prespecified range of parameters at a prespecified rate of variation. The process 100 can be implemented by any of the systems discussed above. The process 100 can be implemented alone or in combination with other processes described herein.

In several embodiments, the process 1000 can begin at block 1002 where the electrode 102 is positioned to stimulate a peripheral nerve. In some instances, the electrode 102 is a component of the device 100. The method moves to block 1004 where the device 100 delivers stimulation to the peripheral nerve for a prespecified time. The method then moves to block 1006 where one or more parameters of the stimulus are varied over a prespecified range of parameter values. In certain embodiments, the one or more parameters are further varied over a prespecified rate of variation.

FIG. 11 illustrates a flow chart of an embodiment of a process 1100 for simultaneously varying multiple stimulation parameters (e.g., parameters A and B) between two or more prespecified values as stimulation is applied to a nerve (e.g., median or radial nerve). The process 1100 can be implemented by any of the systems discussed above. The process 1100 can be implemented alone or in combination with other processes described below.

The process 1100 can begin at block 1102 with selecting a first parameter of a stimulation signal to vary during a prespecified time. Variability can be applied to one or more of the following parameters for stimulating a nerve including but not limited to burst frequency or alternating frequency, pulse frequency, pulse width, pulse spacing, intensity, current amplitude, voltage amplitude, duration of stimulation, on/off periods, or amplitude envelope periods. At block 1104, the method selects a second parameter of the stimulation signal to vary during a prespecified time.

The process moves to block 1106 where the stimulation signal is delivered while simultaneously varying the first and second parameters. The process 1100 can be applied to one or more nerves. For example, parameters A and B can be varied for a first nerve (e.g., median nerve) and for a second nerve (e.g., radial nerve). Of course, the values of the parameters for the first nerve need not be the same as the values of the parameters for the second nerve.

In certain embodiments, the same parameters (e.g., parameters A and B) are varied across at least two nerves. In other embodiments, the parameters varied across the first nerve are different from the parameters varied across the second nerve. In certain embodiments, the process 1100 can be implemented by alternating stimulation between multiple nerves with a specific burst frequency or used to stimulate a single nerve. In certain embodiments where multiple nerves are stimulated, the stimulation parameters can be varied for stimulation of the first nerve but may be fixed for stimulation of the second nerve.

FIG. 12 illustrates a flow chart of an embodiment of a process 1200 for alternately varying multiple stimulation parameters (e.g., parameters A and B) between two or more prespecified values as stimulation is applied to a nerve (e.g., median or radial nerve). The process 1200 can be implemented by any of the systems discussed above. The process 1200 can be implemented alone or in combination with other processes described below.

The process 1200 can begin at block 1202 with selecting a first parameter of a stimulation signal to vary during a prespecified time. Variability can be applied to one or more of the following parameters for stimulating a nerve including but not limited to burst frequency or alternating frequency, pulse frequency, pulse width, pulse spacing, intensity, current amplitude, voltage amplitude, duration of stimulation, on/off periods, or amplitude envelope periods. At block 1204, the method selects a second parameter of the stimulation signal to vary during a prespecified time.

The process moves to block 1206 where the stimulation signal is delivered while alternating between varying each of the first and second parameters. The process 1200 can be applied to one or more nerves. For example, parameters A and B can be varied for a first nerve (e.g., median nerve) and for a second nerve (e.g., radial nerve). Of course, the values of the parameters for the first nerve need not be the same as the values of the parameters for the second nerve.

In certain embodiments, the same parameters (e.g., parameters A and B) are varied across at least two nerves. In other embodiments, the parameters varied across the first nerve are different from the parameters varied across the second nerve. In certain embodiments, the process 1200 can be implemented by alternating stimulation between multiple nerves with a specific burst frequency or used to stimulate a single nerve. In certain embodiments where multiple nerves are stimulated, the stimulation parameters can be varied for stimulation of the first nerve but may be fixed for stimulation of the second nerve.

FIG. 13 illustrates a flow chart of an embodiment of a process for varying multiple stimulation parameters (e.g., parameters A and B) between two or more prespecified values by applying different timescales to each parameter as stimulation is applied to a nerve (e.g., median or radial nerve). The process 1300 can be implemented by any of the systems discussed above. The process 1300 can be implemented alone or in combination with other processes described below.

The process 1300 can begin at block 1302 with selecting a first parameter of a stimulation signal to vary during a prespecified time. Variability can be applied to one or more of the following parameters for stimulating a nerve including but not limited to burst frequency or alternating frequency, pulse frequency, pulse width, pulse spacing, intensity, current amplitude, voltage amplitude, duration of stimulation, on/off periods, or amplitude envelope periods. At block 1304, the method selects a second parameter of the stimulation signal to vary during a prespecified time.

The process moves to block 1306 where the stimulation signal is delivered to a peripheral nerve. While the stimulation signal is being delivered, the first parameter of the stimulation signal is varied on a first timescale at block 1308 and the second parameter of the stimulation signal is varied on a second timescale at block 1310. The process 1100 can be applied to one or more nerves. In this way, in certain embodiments, blocks 1306,1308, and 1310 are performed concurrently.

In certain embodiments, parameters A and B can be varied for a first nerve (e.g., median nerve) and for a second nerve (e.g., radial nerve). Of course, the values of the parameters for the first nerve need not be the same as the values of the parameters for the second nerve.

In certain embodiments, the same parameters (e.g., parameters A and B) are varied across at least two nerves. In other embodiments, the parameters varied across the first nerve are different from the parameters varied across the second nerve. In certain embodiments, the process 1300 can be implemented by alternating stimulation between multiple nerves with a specific burst frequency or used to stimulate a single nerve. In certain embodiments where multiple nerves are stimulated, the stimulation parameters can be varied for stimulation of the first nerve but may be fixed for stimulation of the second nerve.

FIG. 14 illustrates an architecture 1400 for determining a method that varies multiple stimulation parameters based on adaptive learning. The architecture 1400 illustrated in FIG. 14 can be employed in combination with one or more of the processes 1100-1300 discussed above. In certain embodiments, the processes 1100-1300 correspond to blocks 1402,1404,1406 in FIG. 14, respectively. In certain embodiments, block 1408 can correspond to a method for varying stimulation patterns across a nerve that is not the same as the methods corresponding to blocks 1402,1404,1406. For example, the method associated with block 1408 can begin as one of the methods associated with blocks 1402-1406 but was subsequently adjusted or modified based on blocks 1412 and/or 1414.

The architecture 1400 further includes block 1410 where adaptive learning is employed to select a process from the processes 1402-1408 for use during a therapy session at block 1416. In certain embodiments, the adaptive learning determination 1410 is performed by the learning algorithm 222. The learning algorithm 222 can include programmed instructions for performing processes as discussed herein for detection of input conditions, processing data, and control of output conditions. The learning algorithm 222 can be executed by the one or more hardware processors of the neuromodulation (e.g., neurostimulation) device 100 alone or in combination with the base station 150, the user interface device 150, and/or the cloud 122.

At block 1410, the adaptive learning determination can leverage kinematic measurements 1412 as well as satisfaction data 1414. The kinematic measurements 1412 can include but is not limited to accelerometer or gyroscope data from the sensors 112 (e.g., IMU). In certain embodiments, the kinematic measurements 1412 can include test kinematic data taken during a therapy session. In certain embodiments, the kinematic measurements 1412 can include passive kinematic data. Passive kinematic data is data collected at times outside of the therapy session.

In some embodiments, the neuromodulation, e.g., neurostimulation device 100 or the user interface device 150 with sensors can collect kinematic measurements 1412 (test and/or passive data), or data from other sensors, can measure data over a longer period of time, for example 1, 2, 3, 4, 5, 10, 20, 30 weeks, 1, 2, 3, 6, 9, 12 months, or 1, 2, 3, 5.10 years or more or less, or ranges incorporating any two of the foregoing values, to determine features, or biomarkers, associated with the onset of tremor diseases, such as essential tremor, Parkinson's disease, dystonia, multiple sclerosis, Lyme disease, etc. Biomarkers could include specific changes in one or more features of the data over time, or one or more features crossing a predetermined threshold. In some embodiments, features of tremor inducing tasks have been stored on the neurostimulation device 100 and used to automatically activate sensors when those tremor inducing tasks are being performed, to measure and store data to memory during relevant times.

The devices, systems and methods described above and in the claims are used, in several embodiments to treat depression (including but not limited to post-partum depression, depression affiliated with neurological diseases, major depression, seasonal affective disorder, depressive disorders, etc.). Inflammation is also treated in some embodiments, including but not limited to inflammatory gastrointestinal disorders and skin disorders. In one embodiment, Lyme disease and chronic fatigue syndrome are treated (including chronic inflammatory states and symptoms). Neurological diseases (such as Parkinson's and Alzheimer's) as well their associated symptoms and manifestations are treated in several embodiments (such as depression, tremor, movement disorders, stroke etc.). In some embodiments, rheumatoid arthritis, multiple sclerosis, psoriatic arthritis, osteoarthritis, and psoriasis are treated. Cardiac conditions (such as atrial fibrillation) may also be treated via neuromodulation, as described in several embodiments herein. Headache disorders, such as migraine, are treated in other embodiments. Systems and methods to reduce habituation and/or tolerance to stimulation are provided in several embodiments by, for example, introducing variability in stimulation parameter(s), as described herein. Habituation and/or tolerance to neurostimulation that occur in the treatment of movement, inflammatory, neurological and psychiatric disorders are treated in several embodiments.

The satisfaction data 1414 can include but is not limited to subjective data provided by the user. The subjective data can relate to pre or post treatment and/or patient activities of daily living (ADL). In certain embodiments, the patient inputs a value that reflects a level of satisfaction. The level of satisfaction can be selected from a predetermined range. In certain embodiments, the range is from 1 to 4. Of course, the range can be any range and is not limited to 1 to 4. For example, the user can provide input to the user interface 212 in the form of a patient session impression of improvement (PSII) score and/or a patient satisfaction scope.

In certain embodiments at block 1410, the learning algorithm 222 determines a level of patient therapeutic benefit based on the passive kinematic measurements 1412 without requiring the patient to input a subjective satisfaction level. In certain embodiments, the learning algorithm 222 receives the kinematic measurements 1412 measured during the therapy session along with satisfaction data 1414 input by the user. In this way in certain embodiments, the learning algorithm 222 can determine a level of patient therapeutic benefit based on both the passive kinematic data and the patient provided subjective satisfaction level.

At block 1410, the learning algorithm 222 can select from processes 1402-1408 for varying parameter(s) employed during therapy session based on adaptive learning to improve tremor therapeutic treatment. In certain embodiments, the learning algorithm 222 can select from a plurality of stimulation parameters (e.g., burst frequency and pulse frequency) to vary one parameter across one or more nerves (e.g., median and/or radial nerve) and/or select multiple stimulation parameters to vary across one or more nerves.

In certain embodiments, the plurality of stimulation parameters accessed by the learning algorithm 222 can be a subset of all of the stimulation parameters and or patterns of applying stimulation parameters. For example, in certain embodiments, the learning algorithm 222 selects from the processes 1402-1408 for which a positive outcome is predicted by the learning algorithm 222. In certain embodiments, the learning algorithm 222 modifies the one or more parameters of the selected process based on the individual patient to further personalize the stimulation parameters. In certain embodiments, the learning algorithm 222 can automatically determine a correlation between the satisfaction data 1414 and/or the kinematic measurements 1412 and neurostimulation therapy outcomes to select from the processes 1402-1408.

In several embodiments, neuromodulation, such as neurostimulation, is used to replace pharmaceutical agents, and thus reduce undesired drug side effects. In other embodiments, neuromodulation, such as neurostimulation, is used together with (e.g., synergistically with) pharmaceutical agents to, for example, reduce the dose or duration of drug therapy, thereby reducing undesired side effects. Undesired drug side effects include for example, addiction, tolerance, dependence, GI issues, nausea, confusion, dyskinesia, altered appetite, etc.

When a feature or element is herein referred to as being “on” another feature or element, it can be directly on the other feature or element or intervening features and/or elements may also be present. In contrast, when a feature or element is referred to as being “directly on” another feature or element, there are no intervening features or elements present. It will also be understood that, when a feature or element is referred to as being “connected”, “attached” or “coupled” to another feature or element, it can be directly connected, attached or coupled to the other feature or element or intervening features or elements may be present. In contrast, when a feature or element is referred to as being “directly connected”, “directly attached” or “directly coupled” to another feature or element, there are no intervening features or elements present. Although described or shown with respect to one embodiment, the features and elements so described or shown can apply to other embodiments. It will also be appreciated by those of skill in the art that references to a structure or feature that is disposed “adjacent” another feature may have portions that overlap or underlie the adjacent feature.

Terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. For example, as used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items and may be abbreviated as “/”.

Spatially relative terms, such as “under”, “below”, “lower”, “over”, “upper” and the like, may be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is inverted, elements described as “under” or “beneath” other elements or features would then be oriented “over” the other elements or features. Thus, the exemplary term “under” can encompass both an orientation of over and under. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly. Similarly, the terms “upwardly”, “downwardly”, “vertical”, “horizontal” and the like are used herein for the purpose of explanation only unless specifically indicated otherwise.

Although the terms “first” and “second” may be used herein to describe various features/elements (including steps), these features/elements should not be limited by these terms, unless the context indicates otherwise. These terms may be used to distinguish one feature/element from another feature/element. Thus, a first feature/element discussed below could be termed a second feature/element, and similarly, a second feature/element discussed below could be termed a first feature/element without departing from the teachings of the present invention.

Throughout this specification and the claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising” means various components can be co-jointly employed in the methods and articles (e.g., compositions and apparatuses including device and methods). For example, the term “comprising” will be understood to imply the inclusion of any stated elements or steps but not the exclusion of any other elements or steps.

As used herein in the specification and claims, including as used in the examples and unless otherwise expressly specified, all numbers may be read as if prefaced by the word “about” or “approximately,” even if the term does not expressly appear. The phrase “about” or “approximately” may be used when describing magnitude and/or position to indicate that the value and/or position described is within a reasonable expected range of values and/or positions. For example, a numeric value may have a value that is +/−0.1% of the stated value (or range of values), +/−1% of the stated value (or range of values), +/−2% of the stated value (or range of values), +/−5% of the stated value (or range of values), +/−10% of the stated value (or range of values), etc. Any numerical values given herein should also be understood to include about or approximately that value, unless the context indicates otherwise. For example, if the value “10” is disclosed, then “about 10” is also disclosed. Any numerical range recited herein is intended to include all sub-ranges subsumed therein. It is also understood that when a value is disclosed that “less than or equal to” the value, “greater than or equal to the value” and possible ranges between values are also disclosed, as appropriately understood by the skilled artisan. For example, if the value “X” is disclosed the “less than or equal to X” as well as “greater than or equal to X” (e.g., where X is a numerical value) is also disclosed. It is also understood that the throughout the application, data is provided in a number of different formats, and that this data, represents endpoints and starting points, and ranges for any combination of the data points. For example, if a particular data point “10” and a particular data point “15” are disclosed, it is understood that greater than, greater than or equal to, less than, less than or equal to, and equal to 10 and 15 are considered disclosed as well as between 10 and 15. It is also understood that each unit between two particular units are also disclosed. For example, if 10 and 15 are disclosed, then 11,12,13, and 14 are also disclosed.

Although various illustrative embodiments are described above, any of a number of changes may be made to various embodiments without departing from the scope of the invention as described by the claims. For example, the order in which various described method steps are performed may often be changed in alternative embodiments, and in other alternative embodiments one or more method steps may be skipped altogether. Optional features of various device and system embodiments may be included in some embodiments and not in others. Therefore, the foregoing description is provided primarily for exemplary purposes and should not be interpreted to limit the scope of the invention as it is set forth in the claims.

The examples and illustrations included herein show, by way of illustration and not of limitation, specific embodiments in which the subject matter may be practiced. As mentioned, other embodiments may be utilized and derived there from, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Such embodiments of the inventive subject matter may be referred to herein individually or collectively by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept, if more than one is, in fact, disclosed. Thus, although specific embodiments have been illustrated and described herein, any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description. The methods disclosed herein include certain actions taken by a practitioner; however, they can also include any third-party instruction of those actions, either expressly or by implication. For example, actions such as “percutaneously stimulating an afferent peripheral nerve “includes” instructing the stimulation of an afferent peripheral nerve.”

PARAMETER VARIATION IN NEURAL STIMULATION

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