Patent Grant
11883661
This invention relates generally to Transcutaneous Electrical Nerve Stimulation (TENS) devices that deliver electrical currents across the intact skin of a user via electrodes to provide symptomatic relief of pain. More specifically, this invention relates to apparatus and methods for setting up therapeutic stimulation intensity and therapeutic stimulation patterns based on: a TENS user's electro-tactile sensation threshold; demographic, clinical, and other relevant characteristics; contemporary progress of subjective and objective measures of pain relief, and therapeutic stimulation intensity and patterns preferred by other TENS users. This invention also discloses apparatus and methods for improving the estimation accuracy of a TENS user's electro-tactile sensation threshold.
Transcutaneous electrical nerve stimulation (TENS) is the delivery of electricity across the intact surface of the skin to activate the underlying sensory nerve fibers and trigger various physiological responses. The most common clinical application of TENS is to provide analgesia, for acute or chronic pain. Unlike pharmacological approaches to pain control, TENS has few if any side effects and can be used as needed without supervision of a healthcare professional. There are additional clinical applications of TENS, including reduction in the symptoms of restless leg syndrome, decreased nocturnal muscle cramps, and relief from generalized pruritus. Preferred embodiments of the present invention focus on the treatment of chronic pain, which, for the purposes of the present invention, is considered to be pain on most days for at least 3 months. Common forms of chronic pain are arthritic pain such as in the knee or hip due to osteoarthritis, low back and leg pain such as due to lumbar disc disease or spinal stenosis, nerve pain such as due to diabetes, and generalized pain such as occurs with fibromyalgia.
In conventional TENS, electrodes are placed on the skin of the user within, adjacent to, or proximal to, the area of pain. In fixed-site high-frequency TENS, the electrodes are placed in an anatomically and physiologically optimal area (e.g., the upper calf of the user) that results in widespread analgesia. An electrical circuit generates stimulation pulses with specified characteristics. One or more pairs of electrodes, placed on the patient's skin, transduce the electrical pulses and thereby stimulate underlying nerves to relieve pain.
A conceptual model for how sensory nerve stimulation leads to pain relief was proposed by Melzack and Wall in 1965. Their theory proposes that the activation of sensory nerves (Aβ fibers) closes a “pain gate” in the spinal cord that inhibits the transmission of pain signals carried by nociceptive afferents (C and Aδ fibers) to the brain. In the past 20 years, anatomic pathways and molecular mechanisms that may underlie the pain gate have been identified. Sensory nerve stimulation (e.g., via TENS) activates the descending pain inhibition system, primarily the periaqueductal gray (PAG) and rostroventral medial medulla (RVM) located in the midbrain and medulla sections of the brainstem, respectively. The PAG has neural projections to the RVM, which in turn has diffuse bilateral projections into the spinal cord dorsal horn that inhibit ascending pain signal transmission.
TENS is typically delivered in short discrete pulses, with each pulse typically being several hundred microseconds in duration, at frequencies of between about 10 Hz and about 150 Hz, through hydrogel electrodes placed on the user's body. TENS is characterized by a number of electrical parameters including the amplitude and shape of the stimulation pulse (which combine to establish the pulse charge), the frequency and pattern of the stimulation pulses, the duration of a therapy session, and the interval between therapy sessions. All of these parameters are correlated to the therapeutic dose. For example, higher stimulation amplitude and longer stimulation pulses (i.e., larger pulse charges) increase the therapeutic dose, whereas shorter therapy sessions decrease the stimulation dose. Clinical studies suggest that pulse charge and therapy session duration have the greatest impact on therapeutic dose.
One of most important parameters for properly setting up TENS therapy is the electrical stimulation intensity (also known as the therapy intensity or the therapeutic intensity) which is generally associated with the amplitude of the electrical stimulation pulse. Current literature suggests that the electrical stimulation of TENS therapy should evoke “strong but not painful” sensation to maximize the therapeutic benefit of the TENS therapy. TENS users could seek professional help such as healthcare providers for proper setup of their TENS devices. However, access to such resources may be limited and inconvenient. Therapy outcome (e.g., reduction in pain intensity or pain interference to sleep) is gradual and sometimes hard to measure. Therefore, a TENS user may not be able to tell whether they are benefiting from therapies. The lack of positive and timely feedback often leads to premature termination of the TENS therapy and limits the therapeutic potential of TENS devices.
Some over-the-counter TENS devices such as the Quell® product (Neurometrix Inc., Waltham, MA, USA) provide a two-step calibration process to help TENS users set up the proper therapeutic stimulation intensity. During the calibration process, the user will indicate their first sensation of electrical stimulation when the TENS device gradually ramps up the stimulation level. The user-indicated electro-tactile sensation threshold is then used by the TENS device to automatically set therapeutic stimulation intensity.
Two issues hinder proper setup of the therapeutic stimulation intensity: poor accuracy of the sensation threshold indicated by some users, and inter-subject variations of the relationship between sensation threshold and therapeutic intensity.
While most TENS users can accurately indicate their sensation threshold, some fail to indicate their sensation threshold correctly. Because sensation threshold indication is the first step to be performed after the TENS device and its associated electrodes are placed on the user's body, the sensation from elements such as the cold gel pads of the TENS electrodes may interfere with the user's perception of the electro-tactile stimulation sensation. As a result, the sensation threshold may be indicated by the user at too low or too high a level. Also, some users with prior experience of other TENS devices for which therapeutic intensity is directly set may incorrectly indicate a desired therapeutic intensity instead of the true sensation threshold, resulting in an indicated sensation threshold which is higher than the actual sensation threshold. It is thus important, when using the aforementioned two-step therapy intensity setup, to validate the sensation threshold in real time prior to its use in calculating the therapeutic intensity. It is also desirable to provide an immediate feedback to the user if the indicated sensation threshold is outside an expected range.
While the sensation threshold is the most important predictor of TENS therapeutic intensity for an individual, other factors (such as age, gender, health conditions, chronic pain history, etc.) also affect the therapeutic stimulation intensity which is optimal to individual TENS users. Lack of personalization of mapping a user-indicated sensation threshold to the desired therapeutic intensity may lead to sub-optimal setting of therapeutic intensity. TENS users must then manually adjust their therapeutic intensity levels through trial and error, a time-consuming process. Therefore, it is desirable to create a personalized mapping, from sensation threshold to therapeutic intensity, based on characteristics of each TENS user.
The present invention comprises the provision and use of a novel apparatus and method to maximize the therapeutic benefits of TENS therapy for individual users. Function blocks of the present invention are illustrated in
In one form of the invention, there is provided apparatus for determining a target stimulation intensity level of transcutaneous electrical nerve stimulation to a user that evokes a desired sensation from the user, said apparatus comprising:
In another form of the invention, there is provided apparatus for assessing the accuracy of a target stimulation intensity level used in transcutaneous electrical nerve stimulation to a user, said apparatus comprising:
In another form of the invention, there is provided apparatus for providing transcutaneous electrical nerve stimulation to a user, said apparatus comprising:
In another form of the invention, there is provided apparatus for providing transcutaneous electrical nerve stimulation to a user, said apparatus comprising:
In another form of the invention, there is provided a system for providing transcutaneous electrical nerve stimulation to a user, said system comprising:
These and other objects and features of the present invention will be more fully disclosed or rendered obvious by the following detailed description of the preferred embodiments of the invention, which is to be considered together with the accompanying drawings wherein like numbers refer to like parts, and further wherein:
The present invention comprises the provision and use of a novel TENS device comprising a stimulator designed to be placed on a user's upper calf (or other anatomical location) and a pre-configured electrode array designed to provide electrical stimulation to at least one nerve disposed in the user's upper calf (or other anatomical location). Although the preferred embodiment of the present invention comprises the placement of the TENS device on the upper calf of the user, additional anatomical locations (such as above the knee, on the lower back, and on the upper arm) are contemplated and are also considered to be within the scope of the present invention.
There are various forms of TENS that are primarily differentiated by stimulation characteristics. The most common forms of TENS is “conventional TENS”. In a conventional TENS device, an electrical circuit generates repetitive, discrete stimulation pulses. The pulse waveform specifications include amplitude (usually up to 100 mA), duration (typically 100-500 μsec) and shape (typically monophasic or biphasic). The pulse sequence specifications include the frequency (typically 50-150 Hz) and pattern (e.g., regular, random, bursts). Finally, the therapy schedule is defined by the duration of each therapy session (typically 20-60 minutes of continuous stimulation), and the interval between therapy sessions, which is usually manually controlled by the user but which may be automated (typically every 30-60 minutes). One or more pairs of electrodes, placed on the patient's skin, transduce the electrical pulses provided by the stimulator and thereby stimulate underlying nerves. The clinical efficacy, comfort and efficiency of TENS is dependent on the aforementioned stimulation parameters. For example, to be effective, the stimulation intensity must be above the sensation threshold at a level that feels “strong but comfortable” to the user. Therapy sessions having a length of 60 minutes are more effective than shorter sessions. Finally, the therapy schedule should match the user's pain pattern, which may require therapy sessions throughout the day and night.
While the stimulation intensity is normally associated with the amplitude of an electrical stimulation pulse, the term “intensity” in this application is more generally used to refer to any stimulation characteristics that may impact the effect of electrical pulses on a TENS user or any combination of these characteristics. The characteristics may include the amplitude of an individual stimulation pulse, the width of each pulse, the morphology of the pulse (e.g., rectangular, triangle, trapezoid), the complexity of the pulse (e.g., one rectangular shape followed by another rectangular shape of opposite polarity), the asymmetry of complex pulses (e.g., a second rectangular shape following a first rectangular shape may have its width, or height, or both, different from that of the first rectangular shape), the frequency of the pulses (e.g., fixed or random), patterns of the pulses (e.g., pulses with regular intervals between consecutive pulses (regular) or a group of several pulses with very short intervals between them and then separated by a long interval before another group of pulses is activated (burst)), and the therapy session duration (how long each active stimulation session lasts). The duration of a pulse is the same as the width of the pulse if only one simple geometric shape is in the pulse. The duration of a pulse with two rectangular shapes separated by a quiet period is the summation of the widths of the two rectangular shapes.
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In one preferred form of the invention, stimulator housing 111 also houses a battery 150 (
In another form of the invention, TENS device 100 may comprise more than one stimulator housing 111, e.g., to better conform to the body and/or to improve user comfort by distributing circuitry and portable power source components more evenly around the perimeter of a leg (or other body part).
And in still another form of the invention, a flexible circuit board is used to distribute the TENS stimulation circuitry and other circuitry more evenly around the leg of the user and thereby reduce the thickness of the device.
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The same push button or any other user input means can serve more than one function through context-based interpretation. For example, during the calibration process, a gentle tap to the device housing can serve as an indication of sensing the current stimulation pattern; during “standby” state, the same tap can serve as a start command to initiate a TENS therapy; and during active stimulation state, the same tap can serve as a stop command to stop the ongoing TENS therapy.
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In one preferred form of the invention, TENS device 100 is configured to be worn on the user's upper calf 140 as is shown in
In another preferred form of the invention, TENS device elements 110, 300, and 130 are integrated into one element.
Electrical current (i.e., for therapeutic electrical stimulation to the tissue) is provided to the electrode pairs 304, 306 and 302, 308 by connectors 310, 312 (
In one preferred form of the present invention, the skin-contacting conductive material of electrodes 302, 304, 306, 308 is a hydrogel material which is “built into” electrodes 302, 304, 306, 308. The function of the hydrogel material on the electrodes is to serve as an interface between the electrodes 302, 304, 306, 308 and the skin of the user (i.e., within, or adjacent to, or proximal to, the portion of the user's body in which the sensory nerves which are to be stimulated reside). Other types of electrodes such as dry electrodes and non-contact stimulation electrodes have also been contemplated and are considered to be within the scope of the present invention.
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It should be appreciated that processor 515 may comprise a general purpose microprocessor (CPU) of the sort well known in the art together with appropriate programming to provide the functionality disclosed herein, including, among other things, providing the functionality for determining sensation threshold, the functionality for determining initial therapeutic intensity level, the functionality for determining subsequent intensity level adjustment, the functionality for determining the therapy schedule, etc.
In prior U.S. patent application Ser. No. 13/678,221, filed Nov. 15, 2012 by Neurometrix, Inc. and Shai N. Gozani et al. for APPARATUS AND METHOD FOR RELIEVING PAIN USING TRANSCUTANEOUS ELECTRICAL NERVE STIMULATION, issued as U.S. Pat. No. 8,948,876 on Feb. 3, 2015, which patent is hereby incorporated herein by reference, apparatus and methods are disclosed for allowing a user to personalize the TENS therapy stimulation intensity according to the electrotactile perception threshold of the user at the time of the setup of the TENS device. The aforementioned U.S. Pat. No. 8,948,876 also discloses apparatus and methods to automatically restart additional therapy sessions after an initial manual start by the user.
In prior U.S. patent application Ser. No. 14/230,648, filed Mar. 31, 2014 by NeuroMetrix, Inc. and Shai Gozani et al. for DETECTING CUTANEOUS ELECTRODE PEELING USING ELECTRODE-SKIN IMPEDANCE, issued as U.S. Pat. No. 9,474,898 on Oct. 25, 2016, which patent is hereby incorporated herein by reference, apparatus and methods are disclosed which allow for the safe delivery of TENS therapies at night when the user is asleep. These methods and apparatus allow the TENS device to be worn by a user for an extended period of time, including 24 hours a day.
There is no single (i.e., universal) TENS stimulation intensity that provides an effective, yet tolerable (i.e., not painful), therapeutic dose for all users. Therefore, in order to obtain the clinical benefit of TENS therapy, it is essential to set the therapeutic stimulation intensity to a user-specific level. A stimulation intensity that elicits a “strong but not painful” sensation will provide effective pain relief, and is therefore suggestive of an intensity that is within the therapeutic window. The traditional approach in TENS is for medical staff to train TENS users (i.e., patients) on how to manually increase the intensity until they perceive the “strong but not painful” sensation. It is then the responsibility of the user to perform this procedure as necessary, e.g., at home when TENS therapy is needed. In addition to using expensive and often inaccessible medical resources, this approach is error prone inasmuch as users may forget how to determine an appropriate therapeutic intensity. A major objective of the present invention is, therefore, to automatically and reliably set the stimulation intensity within the therapeutic range.
The present invention discloses a method for automatically setting the stimulation intensity to a therapeutic level with a sensation that is “strong but not painful”. The method is based on the concept of mapping the user's electrotactile perception scale, on which the “strong but not painful” sensation is represented, to an electrical stimulation intensity scale as measured in milliamps. In this respect, the term “electrotactile” refers to a user's sensation of electrical stimulation. There are three key measurable electrotactile perception levels: the electrotactile sensation threshold (electrotactile sensation threshold refers to the first sensation of electrical stimulation by a user), the electrotactile pain threshold (electrotactile pain threshold refers to the first pain sensation by a user as a result of electrical stimulation), and the electrotactile tolerance threshold (electrotactile tolerance threshold is the maximum electrotactile pain sensation that a user will tolerate voluntarily). An optimal TENS stimulation intensity is between the electrotactile sensation threshold and the electrotactile pain threshold.
Although the identification of the electrotactile sensation threshold is the primary focus of the preferred embodiment as described in this application, it should be understood that the identification of other target thresholds or desired sensations (such as the electrotactile pain threshold, the electrotactile tolerance threshold, the strong but not painful sensation level, and the strong but comfortable sensation level) are equally applicable with the method described in this application.
The problem of identifying the stimulation intensity threshold at which a user can sense the stimulation can be formulated as a task of finding the position of a target value within a sorted array. The target value is the minimum intensity level that can be perceived reliably by a user. The sorted array is a list of ordered intensity levels where the order is based on the likelihood that the stimulation intensity will invoke an electrotactile sensation from a user. The most efficient search algorithm is a binary search algorithm. However, the decision of labeling each intensity level as either above or below the electrotactile sensation threshold is not perfect (i.e., the process is noisy, with a certain level of error rate). Probabilistic bisection search algorithms (also known as noisy binary search algorithms) are designed to deal with the situation where the algorithm cannot reliably compare elements of the array or label elements with certainty.
The preferred embodiment of the present invention uses the probabilistic bisection search algorithm to determine the electrotactile sensation threshold based on a user's reaction to the presentation of a collection of electrical stimulation pulses with different stimulation intensity levels.
It can be shown that the optimal method of finding a unique point X* (the electrotactile sensation threshold intensity for an individual) in a range (all of the possible sensation threshold intensity values or the stimulation intensity range of the TENS device) is a bisection algorithm, in which a perfect information source is queried repeatedly to learn whether a given point x is to the left or the right of X*. This has the effect of halving the size of the search space at each iteration. However, if the information source is imperfect, i.e., responses from the information source are noisy, and therefore only correct with some probability p, where ½<p<1, then the bisection algorithm may choose the wrong path at any iteration, leading to an incorrect result. In the context of calibration, some TENS users are unable to consistently indicate correct classifications as to whether a given stimulation intensity is above or below electrotactile sensation. Therefore, it is important to develop apparatus and method to robustly estimate sensation threshold under the assumption that user indications may be incorrect from time to time.
To account for a noisy or imperfect response from TENS users during a calibration process, we can modify the binary search approach such that at each iteration, we bisect the probability space of instead, using Bayes' rule to update the posterior probability density (R. Waeber, P. Frazier, and S. Henderson. Bisection Search with Noisy Responses. In SIAM Journal on Control and Optimization. May 20, 2013; 51(3)). In the general case, we can assume that the probability density function (PDF) Fn is a uniform distribution over
. However, later results will show that we can achieve a more accurate and faster-converging calibration with a distribution that approximates the PDF of the sensation threshold (ST) over the user population or the PDF of the ST tailored to an individual based on demographic and clinical characteristics (i.e., user profile).
After each iteration n, the median of the cumulative density function Fn provides a new estimate of X* for the (n+1)th query. It can be shown that the residuals, ε[|X*−Xn|], converge to 0 at a geometric rate or better. For each iteration, the user provides a response as to whether he has sensed the stimulation at a given intensity Xn, Zn(Xn)∈{−1,+1}. The result Zn(Xn)=+1 (not able to sense the stimulation) indicates X* is to the right of Xn, and Zn(Xn)=−1 indicates X* is to the left of Xn (able to sense the stimulation). Z can be thought of as a random Bernoulli variable.
In a preferred embodiment of the present invention, we consider the user's indication as noisy responses to queries and individual test currents (i.e., various stimulation currents provided by TENS device 100) to be the queries. The user is instructed to respond (e.g., by pressing a mechanical button or by tapping a button on a smartphone screen or any other indication means as disclosed) when the electrotactile stimulation is felt. If the user does not press the button at a given level Xn within a time window, this is taken to mean that the sensation threshold, X*, is likely to be greater than Xn (i.e. Zn(Xn)=+1). Otherwise, if the user presses the button, X* is too high and Zn(Xn)=−1. The time window for indication can be fixed, varied by the user's profile (e.g., demographic and clinical characteristics or other factors such as prior experience with TENS devices), test current intensity, prior response time by the user, etc.
After each iteration at Xn=x, the posterior density function fn+1 is updated according to Equation 1, which is essentially an application of Bayes' rule:
Unlike the ramping method previously disclosed in the aforementioned U.S. patent application Ser. No. 13/678,221, issued as U.S. Pat. No. 8,948,876 (and which is incorporated herein by reference), which presents the user with a gradually increasing stimulation intensity until the user indicates that the sensation threshold (ST) has been reached, a classical PBA method moves freely above and below the midpoint of the CDF. Although this represents a potential means of reducing a user's tendency to overestimate their ST, it may also lead to queries significantly above the user's pain threshold, which results in the user being subjected to pain during the calibration process (i.e., the process to determine the user's ST). Conversely, queries which are significantly below the user's ST add to the calibration time, which is also undesirable. To overcome these limitations, another embodiment is proposed as follows:
For n=0:
For iteration n+1:
Based on data from a study of 15 subjects, there does not seem to be a strong correlation between pain threshold and sensation threshold (ST), though this could be explained by the subjectivity of pain.
Based on the above, this generation of the algorithm uses a ramp for subsequent iterations of test current if and only if:
As it will be disclosed below, the sensation threshold intensity (the first intensity to evoke electrotactile sensation) and the therapeutic intensity (the strong yet not painful stimulation) can be predicted based on user profile (e.g., demographic and clinical characteristics). In another embodiment, intensity levels of test current stimulation for a user is constructed based on predicted ranges of sensation threshold intensity and therapeutic intensity. As an example, the first test current intensity is set to 75% of the sensation threshold intensity predicted for the user if the user provides his user profile information. The first test current intensity is set to 50% of the population mean if the user fails to provide his user profile information. In subsequent presentation of test current, the intensity IL(k+1) shall be two times of the previously presented test current intensity IL(k) if the user indicates that the previously presented current intensity cannot be felt: IL(k+1)=2*IL(k). However, the intensity level (i.e., two times the previously presented test current intensity) is modified (i.e., reduced) if such an intensity exceeds a scaled version predicted therapeutic intensity PTI (e.g., 1.25 times of the therapeutic intensity) for this user based on his user profile or such an intensity exceeds predicted pain threshold based on his user profile or an pre-set safety threshold. In one embodiment of IL(k+1) modification, the test current intensity is reduced by 10%: IL(k+1)=0.9*2*IL(k). In another embodiment of IL(k+1) modification, the test current intensity is reduced to 110% of the predicted therapeutic intensity: IL(k+1)=1.1*PTI.
Generally speaking, PBA algorithms begin with a uniform distribution of values, as it is assumed that all numbers are equally likely in the space prior to beginning the search. In the context of sensation threshold calibration, this implies that all valid stimulation intensity levels are equally likely to be the sensation threshold for TENS user population. However, this may not be the case with the sensation threshold (ST), and further, this distribution (likelihood function) can be estimated from existing data.
Finally, it should also be noted that these two probability distributions are not the only possibilities. In an enhanced calibration where user demographics are available, information entered could be used to select a priori from a set of known distributions (i.e., a distribution for diabetics, a distribution for fibromyalgia sufferers, etc.). While the algorithm is able to converge in most cases regardless of the starting distribution, this could be a means to shorten the length of calibration and make it as brief as possible.
A prior distribution function for sensation threshold (ST) can be constructed based on various data sources or a combination of data sources. One source is a controlled study designed for collecting sensation threshold data from volunteer subjects. Data can also be from a registered database where current TENS device users store their TENS device usage data and consent to allow usage of their de-identified data for product improvement and scientific research. Sensation threshold probability distribution functions can be constructed from these datasets for TENS users based on their age, gender, body mass index, painful health conditions, chronic pain locations, pain frequency, pain patterns, sensitivity to weather and weather changes, pain ratings (average and worst pain) and pain interference scores (interference with sleep, activity, and mood), body temperature, other demographic and health conditions, and environmental conditions such as air temperature and humidity. The above referenced factors plus others commonly known in clinical literature as factors to influence electrotactile sensation are collectively referred to as elements of a user profile. A population-based ST probability distribution or likelihood function can be further modified for each user based on elements of the user profile to create a more personalized a prior ST probability distribution to guide the query process of the PBA algorithm.
The calibration algorithm should, ideally, have the following desired properties:
Apart from the distribution choice described previously, there are two parameters to be selected which affect the speed of convergence and number of button presses required: p, the likelihood of the user giving the “correct” answer; and max(fn), the threshold for the maximum peak in the PDF at which we declare the algorithm to have converged.
Overestimating p leads to an overly aggressive Bayesian update according to Eq. 1. This may mean that variations in the user's decisions of when to press the button cause slower-than-expected convergence in the error. Similarly, underestimating p does not provide a robust adjustment to the PDF at each iteration, again leading to slow convergence. Both types of mis-estimation can lead to an increased error in the final calibration result.
Based on an extensive simulation study, p=0.79 gives the best tradeoff between speed and accuracy and is used in a preferred embodiment of the present invention.
In another embodiment, the p value can be dynamically determined based on the timing of the user response at each query point. For example, if the user responds with a button press as soon as stimulation with a (higher) new intensity is delivered, it can be interpreted that the user is fairly certain with his response. Thus a higher p value can be used to update the PDF. On the other hand, if the user responds with a longer delay after stimulation (but still within the prescribed time period), a lower p value may be used to update the PDF.
In another embodiment, response time is compared with a relative response time. Instead of comparing a user's response time to an absolute response time reference as described above, his response time is compared with a typical response time of other users. If the user responds with a time shorter than the typical time, it can be interpreted that the user is fairly certain with his response and a higher p value can be used to update the PDF. Similarly, if the user's response time is longer than the typical time (but still within the prescribed time period), a lower p value is used to update the PDF. A typical response time reference for a different stimulation intensity can be compiled based on all available TENS users, or based on TENS users with a specific user profile characteristic (e.g., age, gender, baseline pain, etc.).
As the range is taken to be a continuum, and we are trying to minimize the number of queries/button events, a decision rule is required on when the error is good enough. There are several non-convergence conditions for the algorithm to exit:
Ideally, however, the algorithm should find and compare the largest peak in the distribution to a threshold at each iteration.
In addition to using a uniform distribution function or an actual distribution function estimated from experiment data, one could also modify the actual distribution function to improve the calibration process with a shorter calibration time. In one embodiment, the actual distribution function is broadened at its main peak region via convolution (or a low pass filter), and the probability density values are increased in the lowest regions.
In another embodiment, new exit criteria are used to reduce the number of button presses required to complete the calibration process:
While a uniform distribution function and a numerical probability density function based on actual user data are considered for the sensation threshold distribution, other probability density functions such as Poisson distribution and Gamma distribution are also considered.
When a user detects a target sensation from an electrical stimulation from a TENS device, the user needs to indicate such sensation so that the sensation can be registered for later use. Such an indication can be manual or automated, it can also be done voluntary or involuntarily, subjectively or objectively.
In one preferred embodiment, the indication is done by pushing a button on a physical device (e.g., on TENS device 100 or a smartphone 180 running an appropriate App, etc.). In another embodiment, the indication is accomplished by one or more gestures to a physical device that can detect gestures, such as those with an embedded accelerometer (e.g., carried by TENS device 100 or a smartphone 180 running an appropriate App). The physical device can be with a button or without any mechanical actuator (button), and it can be directly or remotely connected to the stimulation apparatus. In yet another embodiment, the indication is done by tapping the screen or a button on a touch screen of a device (e.g., a smartphone), and the device can be directly or remotely connected to the stimulation apparatus.
The indication can also be detected automatically. When electrical stimulation causes the peripheral sensory nerve to be activated, such sensory activation will result in local or central anatomical responses. Blood flow may increase locally, which can be detected via galvanic skin response sensors or temperature sensors. Local muscle actions may be triggered, particularly for electrotactile pain threshold sensations and electrotactile tolerance threshold sensations. Therefore, an EMG sensor or an electromechanical sensor such as accelerometer can be used to detect local muscle actions and to register the indication automatically. The electromyography (EMG) sensor, a part of electrophysiology sensor 139 (
The indication can also be detected involuntarily through other sensors. Somatosensory evoked potential (a brain neuron cell response to sensory stimulation) can be detected and measured via electroencephalogram (EEG) electrodes and associated bio-amplifier circuitry 151 (
The calibration process disclosed in U.S. patent application Ser. No. 13/678,221, issued as U.S. Pat. No. 8,948,876 (and which is incorporated herein by reference), presents TENS users with stimulation pulses whose amplitude gradually increase with time while other factors (such as pulse duration and frequency) are fixed. This is necessary as the search is a single threshold detection process (i.e., the transition from non-detectable stimulation to detectable stimulation). The present invention discloses an apparatus and method that presents discrete stimulation pulse patterns in order to construct a collection of indications (sensed or not sensed) with respect to the stimulation intensity of the pulse patterns. As a result, one or more parameters of the stimulation pulses can be modified at the same time.
In one preferred embodiment, if the first pulse pattern (with A1 and D1 as the amplitude and duration of the pulses) received a negative indication (no sensation) from the user, both pulse amplitude and duration can be increased for the second pulse pattern (with A2>A1 and D2>D1). If user indication is positive, the third pulse with A1 and D2 will be presented to the user first for his indication if such a combination will consume less battery power than the combination of A2 and D1. Of course, if the third pulse cannot be sensed by the user, the fourth pulse (with A2 and D1) will be presented to the user for indication. The above example illustrates the advantage of discrete pulse pattern presentation in the context of battery power consumption, an important consideration in any portable/wearable device use.
In one preferred embodiment, the determination of which parameter serves as the primary factor to adjust depends upon one or more characteristics of the user. As an example, for a user with age 40 or younger, pulse frequency can be increased to a maximum value first before pulse amplitude is increased when constructing a stimulation pulse pattern with increased intensity. For users with a specific pain condition (e.g., fibromyalgia), pulse duration is preferentially increased before pulse amplitude and frequency.
In the foregoing description, electrotactile sensation threshold is the target sensation for the calibration process. However, other electrotactile sensation levels, such as electrotactile pain threshold, “strong yet not painful” level, and electrotactile tolerance threshold, may be the target sensation for the calibration process. Electrotactile sensation threshold is the lowest stimulation intensity and is generally a well understood concept. Therefore, it is a preferred target in the initial calibration process. However, there are cases when other calibration targets may be needed and the same search process can be used to determine the stimulation pulse intensity that evokes the target sensation.
In one preferred embodiment, a user may request a calibration to “strong yet not painful” sensation after the user has used TENS therapy with a specific pulse pattern (Pattern A) for some time due to nerve habituation (i.e., where the user's nervous system becomes desensitized to a stimulation pattern after repeated exposure to such a stimulation pattern). While increasing pulse amplitude is the most straightforward way to overcome nerve habituation, it may not be most desirable due to energy consumption and further nerve habituation effect. Since the user is familiar with the “strong yet not painful” sensation, calibration can be done directly towards this sensation target but utilizing different stimulation patterns. The calibration process can present a sequence of stimulation patterns with a lower pulse amplitude but with increased pulse duration or increased pulse frequency or altered pulse morphology, or a combination thereof. Once the user identifies the pulse pattern (Pattern B) that gives the same “strong yet not painful” sensation, the pulse pattern will be used for subsequent therapy sessions. After a pre-determined period (e.g., two weeks), the stimulation pulse pattern will revert to Pattern A so that the user does not develop nerve habituation towards Pattern B. Identification and alternating among more than two pulse patterns may also be used.
In another preferred embodiment, alternative pulse patterns that evoke “strong but not painful” sensation are identified not for the purpose of overcoming nerve habituation but for battery power conservation purpose. After identifying the first pulse pattern (with pulse duration D1 and amplitude A1) that evokes “strong but not painful” sensation, the user may request a second calibration if the battery consumption of such a pulse pattern is not optimal. Alternative pulse patterns, such as pulses with longer duration (D2>D1 and D3>D1) but lower amplitude (A2<A1, A3<A1) will be presented to the user. If the user confirms the new pulse pattern (D3 and A3) gives the same sensation as the first pulse pattern (D1 and A1), therapeutic sessions with the new pulse pattern (D3 and A3) will be used to extend battery life between recharges.
In another preferred embodiment, alternative pulse patterns that evoke “strong but not painful” sensation are identified for the purpose of increased comfort during therapy use at night. Two or more pulse patterns can be calibrated to yield the same “strong but not painful” sensation during the day for a user. Overnight therapies with one of identified pulse patterns per night will be delivered, and the sleep quality is used as the tie-breaker selection criterion to determine which pulse pattern is optimal for the user to use at night. We note that in this preferred embodiment, the search criteria are objective measurements of sleep quality (e.g., total sleep time, sleep efficiency, and period leg movement index) instead of subjective perception criteria.
In another preferred embodiment, alternative pulse patterns that evoke “strong but not painful” sensation are identified for the purpose of long-term use comfort for users with certain painful conditions. For example, if most users with fibromyalgia pain prefer therapies with pulse patterns having a lower pulse frequency, then a new fibromyalgia user shall be presented with several stimulation patterns with different pulse frequencies. The user shall then identify one or more pulse patterns that produce a similar “strong but not painful” sensation and the same short-term comfort level. The TENS device shall select the pattern with the lowest pulse frequency for long-term comfort even before the user with fibromyalgia experiences any long-term discomfort resulting from higher pulse frequency stimulation.
While electro-tactile sensation threshold may not be predicted precisely for a TENS user, a range can be specified with high confidence based on the clinical and demographic characteristics of the user. User characteristics may include age, gender, height, weight, body mass index, health conditions (e.g., presence or absence of diabetes, fibromyalgia, previous back injury, etc.), pain locations (e.g., feet/ankle, lower back, arms, hand/wrist, head, etc.), pain duration, pain patterns (e.g., in the morning, all the time, when resting, etc.), pain frequency (e.g., every day, several times a week, rarely, etc.), feeling about pain or pain catastrophizing (e.g., degree of feeling towards statements like “when I am in pain, I become afraid that the pain will get worse”, etc.), weather sensitivity (e.g., rain will worsen the pain, high humidity will worsen the pain, etc.), pain ratings (e.g., pain intensity, pain interference with sleep, activity, and mood, etc.), etc.
Based on sensation threshold data collected from a large number of TENS users, a predictive model is created to estimate the expected sensation threshold range based on TENS users' clinical and demographic characteristics. In a preferred embodiment, the target sensation threshold (TST) model is given as follows:
TST1=16.78+0.14*[Age]+0.09*[Weight]+2.80*[Diabetes]+0.89*[Spinal_Stenosis]+0.78*[Chronic_Regional_Pain]+0.46*[Leg or Foot_Injury]+0.31*[Other Med Hist]+0.32*[PainDuration OneToThree_Years]+0.80*[Hot_Weather]−4.18*[Female]−0.08*[Height]−0.22*[BMI]-0.61*[Herniated_Disc]−1.69*[Shingles]−0.69*[Humid_Weather]
If a user has diabetes, variable [Diabetes] is set to 1. Otherwise it is set to 0. Similarly, if the user is sensitive to humid weather condition (i.e., pain is greater when the humidity is high), variable [Humid_Weather] is set to 1.
Depending upon the number and types of characteristics provided by the TENS user, different predictive models are developed to resolve ambiguity of missing information. In a preferred embodiment, the TST model for TENS users providing only age (in years), gender, and whether they have diabetes or not, is given as follows:
TST2=6.16+0.14*[Age]−4.36*[Female]+2.93*[Diabetes]
In a preferred embodiment, the sensation threshold range is constructed using a target value and a range interval. The target value is given as above. The range for TST2 is defined as 10th to 90th percentile of TST2 values observed from a dataset of 6376 TENS users:
Expected Range for TST2=[0.514*TST2,1.627*TST2].
As an example, for a female TENS user of 60 year old with diabetes, her target sensation threshold is 13.13 milliamp and expected range of the target sensation threshold is between 6.75 milliamp and 21.36 milliamp. Therefore, the set of expected values of the target sensation threshold for this user is all intensity values between 6.75 milliamp and 21.36 milliamp.
The range interval depends upon the amount and type of clinical and demographic characteristics provided by the user. For example, age is found to be a reliable predictor of the sensation threshold, and missing age information will cause the range interval to increase.
In a preferred embodiment, the TENS user enters his/her demographic and clinical characteristic information via the App running on smartphone 180. Prior to first therapeutic use, the TENS user is asked to set up therapeutic intensity by indicating his/her electro-tactile sensation threshold via a pre-defined indication method. The indication method can be a gesture to a physical device remoted connected to the stimulator (e.g., smartphone 180) or an interaction with a mechanical actuator located on the housing of the stimulator. Determination of the electrotactile sensation threshold can be accomplished by utilizing calibration methods such as the probabilistic binary search approach or the gradual intensity ramp up approach. The predictive model calculates the expected sensation threshold range based on information provided by the user. The user-indicated sensation threshold is compared with the expected range. If the sensation threshold falls within the range, the sensation threshold is considered to be accurate and is used by a subsequent prediction model to determine the therapeutic stimulation intensity. If the sensation threshold falls outside the range, a feedback message is given to the user via the App to request another calibration. Other means of feedback include a vibration pattern from the TENS device. The feedback message can be optionally customized to offer targeted hints to the user. For example, if the indicated sensation threshold is below the lower bound of the expected range, the user is prompted to delay the calibration process until the cold sensation of the electrode gel pads disappears (i.e., when the temperature of the electrode gel reaches an equilibrium with the skin temperature). If the indicated sensation threshold is above the upper bound of the expected range, the user is reminded to indicate the first sensation of electrical stimulation, not the preferred sensation for therapeutic stimulation intensity that the user may have previously experienced.
In another embodiment, the indicated sensation threshold (from either a first-time calibration or a subsequent recalibration) is compared with the expected range so that a confidence level (CL) or accuracy is assigned to the sensation threshold. The confidence level is high if the sensation threshold is near the center of the range and low if the sensation threshold is near the edge of the range or outside the range. In the example above, an indicated sensation threshold of 15 milliamp by the female TENS user will be given a high confidence level (e.g., CL=100%) while an indicated sensation threshold of 20 milliamp by the same user will be given a low confidence level (e.g., CL=15%). The confidence level is used in the subsequent prediction model in addition to the value of the sensation threshold. As an example, both sensation threshold level and user profile characteristics contribute to the prediction of the therapeutic stimulation intensity. If the confidence level of the sensation threshold is low, its contribution towards the therapeutic intensity prediction is deemphasized while the user profile elements are emphasized. In the example above, the indicated sensation threshold of 15 milliamp will contribute more towards prediction of the therapeutic intensity prediction than an indicated sensation threshold of 20 milliamp.
In yet another embodiment, a different stimulation pattern (e.g., short pulse width or long pulse width) is selected and presented to TENS users with a specific clinical condition (e.g., with fibromyalgia or with diabetes), or with a combination of clinical conditions, and the sensation threshold is recorded. The sensation threshold is then used in the subsequent prediction model to determine therapeutic stimulation intensity for the selected stimulation pattern for the TENS user with specified clinical conditions.
In yet another embodiment, more than one stimulation pattern (e.g., burst and regular) of stimulation pulses are presented to TENS users with a specific clinical condition (e.g., with prior back injury) and sensation threshold results are recorded for each stimulation pattern. In one embodiment, the stimulation pattern with a lower sensation threshold may be used as the preferred stimulation pattern for a given TENS user and the sensation threshold and stimulation pattern are passed to the prediction model. In yet another embodiment, all sensation thresholds are then used in the subsequent prediction model to determine a preferred stimulation pattern and the associated therapeutic stimulation intensity.
Although there is a strong correlation between sensation threshold and therapeutic stimulation intensity, it is possible and desirable to enhance the prediction accuracy of the therapeutic stimulation intensity by considering a user's clinical and demographic characteristics in addition to the sensation threshold. In a preferred embodiment, a user-indicated sensation threshold (uiST) that is within the expected sensation threshold range is used to predict therapeutic intensity (TI) together with demographic and clinical characteristics of the user. One such prediction model is given below:
TI1=8.74+0.06*[Age]−2.61*[Female]+1.06*[uiST]
In another embodiment, the user-indicated sensation threshold is not used to predict the therapeutic intensity if the indicated sensation threshold is outside the expected sensation threshold for that user. One such prediction model is given below:
TI2=17.93+0.24*[Age]−8.18*[Female]+5.33*[Diabetes]
In yet another embodiment, the user-indicated sensation threshold is used in a prediction model together with the confidence level (CL) associated with the sensation threshold. As an example, a prediction model that combines TI1 and TI2 based on CL can be constructed as follows:
TI=CL*TI1+(1−CL)*TI2
In another embodiment, therapeutic intensity is predicted based on a user's demographic and clinical characteristics without any consideration of the sensation threshold indicated by the user. In yet another embodiment, therapeutic intensity is predicted exclusively based on the indicated sensation threshold.
Patterns of sensation threshold values (in response to multiple stimulation patterns) can also be used to select the stimulation pattern in addition to predicting the therapeutic intensity associated with the selected stimulation pattern. For example, if sensation threshold values are similar (say within 10% of each other) for both short and long duration stimulation pulses, a short duration pulse pattern will be selected and the therapeutic intensity for the selected pattern will be predicted. In another embodiment, the short duration pulse pattern is preferred only when the TENS user possesses certain demographic and/or clinical characteristics (e.g., female with fibromyalgia).
In addition to demographic and clinical characteristics, a TENS user may provide his/her preferred goal for managing chronic pain. For example, if a user emphasizes better sleep as a priority goal, a regular pulse pattern with a lower therapeutic intensity but more frequent sessions can be set for the user.
Therapeutic intensity prediction can also be modified based on contemporary changes experienced by TENS users. As a user receives TENS therapy with the therapeutic stimulation pattern and intensity predicted based on baseline demographic and clinical characteristics, the user may experience changes in pain conditions as measured either subjectively through pain ratings on the App (running on smartphone 180) or objectively through sleep and activity tracking by the TENS device 100. Therapeutic stimulation intensity and pattern can thus be adjusted based on a TENS therapy outcome. For example, if the pain ratings decrease after two weeks of TENS therapy, the therapeutic intensity prediction model can incorporate such a positive trending into its prediction outcome so that the therapeutic intensity for week three is reduced. During the third week with a reduced therapeutic intensity, if the pain ratings hold steady, the same intensity is predicted for subsequent weeks. If the pain ratings increase, previous higher therapeutic intensity is predicted for subsequent weeks. Similarly, if better sleep is an important goal as indicated by the user and sleep tracking results show little progress in sleep quality improvement during the first two weeks of TENS therapy, a higher evening therapeutic intensity is predicted to increase the TENS therapy dosage before bedtime.
Other quantifiable conditions considered include electrode gel material, strap composition and construction, TENS device dimensions, location of TENS device placement, etc. Changes in these conditions may have an impact on how electrical pulses are felt by TENS users. For example, electrode gels with less moisture content may cause electrode-skin contact to be less uniform and results in a slightly more robust sensation of the same electrical stimulation. In one embodiment, electrode gel composition is included in the prediction model. When an electrode with a gel composition different from that used for sensation threshold measurement is used for therapy, the therapeutic intensity model will adjust the intensity accordingly to take into consideration the effect of the gel composition change.
A “health cloud database” refers to any data resource where data from more than one TENS user are stored and can be shared with one or more TENS devices. A health cloud database allows one user to benefit from the experience of other users using the same or similar therapeutic device or modality.
In one preferred embodiment, TENS users share their anonymous data on the health cloud database. Shared data include their demographic and clinical information such as age, gender, weight, health conditions related to chronic pain, pain locations, pain duration, and pain frequency (daily versus several times a week). Users also share their TENS therapy utilization data such as daily therapy sessions, initial therapeutic intensity setup (in relation to their electrotactile sensation threshold), subsequent therapeutic intensity adjustments over time, and time course of TENS therapy use. Shared data may also include subjective feedback of pain intensity and pain interference with sleep, activity, and mood prior to and throughout a TENS therapy use period; objective tracking of health dimensions such as activity level (steps taken), gait patterns, sleep duration, sleep quality, periodic leg movement count over time; and changes in medication intake.
When a user with a TENS therapy duration exceeding a pre-defined threshold (e.g., 60 days) is added to the health cloud database, prediction models are updated based on a new dataset. In one preferred embodiment, the new dataset is the existing dataset plus the newly added user. In another embodiment, the new dataset is the same size as the existing dataset by replacing data from one user in the existing dataset with the data from the newly added user if the newly added user has a better therapeutic outcome (e.g., if sleep quality improvement is more evident). In one preferred embodiment, data (from multiple users) in a dataset for creating prediction models contribute to the models equally. In another embodiment, each user in the dataset will contribute differently with a different weight for each user based on certain criteria. Incorporating each data point using a different weight can be achieved using weighted least square regression. Weight (the level of contribution to prediction models) of each user can be based on effectiveness of the therapy (e.g., a reduction in pain intensity levels between day 60 and day zero of using TENS therapy) in one preferred embodiment. Weight can be based on an objective measure of outcome such as sleep duration changes between week 10 and week one in another embodiment. Weight can also be based on frequency at which therapies were performed by each user.
In one preferred embodiment, the prediction model calculates the initial therapeutic intensity level based on sensation threshold and demographic and clinical characteristics. In another embodiment, the prediction model calculates the therapeutic intensity level adjustment over time based on an initial therapeutic intensity level and the objective tracking of health conditions (e.g., sleep duration and sleep quality measures). In yet another embodiment, the prediction model calculates recommended therapy session count (i.e., the number of therapy sessions occurring during a given time interval) based on sensation threshold, demographic and clinical characteristics, and goal setting priorities (e.g., reducing pain interference with sleep is the top priority). In yet another embodiment, the prediction model calculates recommended therapy session count distribution between daytime use and nighttime use based on sensation threshold, demographic and clinical characteristics, and goal setting priorities (e.g., reducing pain interference with sleep is the top priority).
In one preferred embodiment, a new user is matched with a group of existing users on the health cloud database. “Match” means similar demographic and clinical characteristics such as having the same gender and age difference within 5 years. “Match” could mean pain locations overlap between the new user and the selected group of existing users. “Match” could also mean the new user shares the same goal of “sleep better” with the selected group of existing users. Instead of using the prediction model created based on all available users in the health cloud database, a prediction model created based on only those “matching” users will be used for the new user.
Data from users with unfavorable therapy outcomes can also be used to guide new users to improve their therapeutic outcome. Unfavorable outcomes may be those with no improvement in pain after a fixed period of use (e.g., 60 days). Unfavorable outcomes may also include those who stopped TENS therapy use shortly after first use. In one preferred embodiment, initial therapeutic intensity levels as a function of their indicated sensation threshold and demographic/clinical characteristics is modeled. In another embodiment, therapy session patterns (i.e., how many therapy sessions occur per day within a period of first use) are compiled. In yet another embodiment, therapy session distributions (daytime use versus nighttime use) as a function of therapy goals (e.g., to improve sleep) are calculated.
Because of inter-user variability, a TENS user is not expected to follow the recommendations of prediction models precisely. When the TENS user adjusts therapeutic intensity level or selects a different therapy schedule, their therapeutic setup and usage pattern can be monitored and compared with that of users with unfavorable therapeutic outcome. If a close match is found (by taking into consideration factors such as demographic/clinical characteristics, therapy goal, and other factors (like medication intake, prior experience with pain therapies, etc.), an alert can be issued to the user or their caregivers for possible corrective actions as they are more likely to gain no pain-relieving benefits by continuing the current practice.
Modifications of the Preferred Embodiments
It will be appreciated that the present invention provides a transcutaneous electrical nerve stimulator with a means for determining the proper therapeutic stimulation intensity by an efficient and reliable method to estimate the sensation threshold using a probabilistic model.
When a sensation threshold is determined, a likelihood of accuracy can be estimated. A low likelihood of accuracy will trigger a feedback to the user to suggest a re-calibration. If a user agrees to recalibrate, the same Probabilistic Bisection Algorithm (PBA) is used in one preferred embodiment. In another embodiment, a different calibration procedure other than the Probabilistic Bisection Algorithm (PBA) is used. Instead of presenting stimulation pulses with discrete and disjoint intensity levels as in PBA, stimulation pulses with continuously increasing intensity levels are presented to the TENS user until the user indicates a sensation is felt. In another embodiment, stimulation patterns with gradually increasing intensity levels are presented to the user to determine the sensation threshold during the first calibration round. If the user fails to indicate a sensation threshold with an acceptable likelihood of accuracy, the PBA method is used in a second calibration round.
To determine the likelihood of accuracy, the sensation threshold is compared against a likelihood function. When the likelihood function L(ST) value at the selected sensation threshold ST is below a threshold RC, a re-calibration suggestion is issued to the user. The likelihood function L(ST) can take a form similar to the initial PDF or a different form. The L(ST) can take the same value for a range of ST values (above the RC value) and a different value (below the RC value) for other ST values. The likelihood function L(ST) can take the form of a continuous function (such as Gaussian function). The likelihood function can be the same for all users or the function specifications can depend upon demographic and clinical factors such as age, gender, body mass index, number and type of painful medical conditions, location of pain, frequency of pain, and patterns of pain. Other factors such as pain sensitivity to weather conditions and pain durations are also considered. Pain ratings (average pain, worse pain) and pain interference levels (with sleep, activity, mood, etc.) are also considered. The recalibration threshold RC depends on the choice of likelihood function. Additionally, the RC (for the same likelihood function choice) may depend upon the calibration history path (see
Once the sensation threshold for a TENS user is estimated, therapeutic stimulation intensity is then calculated for the user. In one preferred embodiment, the therapeutic stimulation intensity is 5 dB above the sensation threshold. In another embodiment, a linear mapping of the form below is used.
[Therapeutic Intensity]=Offset+Scale*[Sensation Threshold]
In a preferred embodiment, the coefficient Scale is a constant for all users. In yet another embodiment, the coefficient Scale is a function of user's demographic and clinical factors.
In another embodiment, therapeutic stimulation intensity is estimated based on both sensation threshold and demographic and clinical factors, including baseline pain ratings. To account for the logarithmic behavior of the electrical stimulation effect, logarithmic transformation for [Therapeutic Intensity] and [Sensation Threshold] is used in another embodiment (represented by log(Therapy) and log(SenTh)):
log(Therapy)=ScaleA*log(SenTh)+Fun(BaseCovariates)+ScaleB
Fun(BaseCovariates) is a function that maps demographic, clinical, and baseline pain rating factors to an incremental value to [Therapeutic Intensity]. In one embodiment, the function is a linear function. In another embodiment, the function is a nonlinear function. In yet another embodiment, the function is a nonlinear fuzzy function.
In a preferred embodiment, datasets from all available users are used to determine the parameters and coefficients for functions, thresholds, and probabilities. In another embodiment, only users who meet certain criteria are included in the datasets. Examples of such criteria include limiting users to those who used the TENS device for a minimum number of days (for example, 8 weeks), and/or to those who experienced a minimum reduction in pain ratings over a period of time (for example, 2 or more point reduction in pain based on 11-point visual analog scale for pain over 10 week period), and/or to those who were able to reduce their intake of prescription pain medications by 25% or more, and/or to those who improved their sleep efficiency by 10%, and/or to those who experienced 15% or more improvement in patient global impression of change (PLIC).
Furthermore, it should be understood that many additional changes in the details, materials, steps and arrangements of parts, which have been herein described and illustrated in order to explain the nature of the present invention, may be made by those skilled in the art while still remaining within the principles and scopes of the invention.
This patent application: (i) claims benefit of prior U.S. Provisional Patent Application Ser. No. 62/892,627, filed Aug. 28, 2019 by Neurometrix, Inc. and Rebecca A. Burrell et al. for METHOD AND APPARATUS FOR EFFICIENT DETERMINATION OF SENSATION THRESHOLD IN TRANSCUTANEOUS ELECTRICAL NERVE STIMULATOR; and(ii) claims benefit of prior U.S. Provisional Patent Application Ser. No. 62/776,834, filed Dec. 7, 2018 by Neurometrix, Inc. and Xuan Kong et al. for INTELLIGENT DETERMINATION OF THERAPEUTIC STIMULATION INTENSITY FOR TRANSCUTANEOUS ELECTRICAL NERVE STIMULATION. The two (2) above-identified patent applications are hereby incorporated herein by reference.
| Number | Name | Date | Kind |
|---|---|---|---|
| 1741962 | Theodoropulos | Dec 1929 | A |
| 4290431 | Herbert et al. | Sep 1981 | A |
| D263869 | Sumiyasu | Apr 1982 | S |
| 4503863 | Katims | Mar 1985 | A |
| 4605010 | McEwen | Aug 1986 | A |
| 4738250 | Fulkerson et al. | Apr 1988 | A |
| 4777711 | Forkner et al. | Oct 1988 | A |
| 4926863 | Alt | May 1990 | A |
| 4989605 | Rossen | Feb 1991 | A |
| 5048523 | Yamasawa et al. | Sep 1991 | A |
| 5063929 | Bartelt et al. | Nov 1991 | A |
| D323561 | Bartelt et al. | Jan 1992 | S |
| 5121747 | Andrews | Jun 1992 | A |
| 5169384 | Bosniak et al. | Dec 1992 | A |
| D342571 | Givens, Sr. | Dec 1993 | S |
| D346029 | Shalvi | Apr 1994 | S |
| 5350414 | Kolen | Sep 1994 | A |
| 5429589 | Cartmell et al. | Jul 1995 | A |
| 5479939 | Ogino | Jan 1996 | A |
| 5487759 | Bastyr et al. | Jan 1996 | A |
| 5562718 | Palermo | Oct 1996 | A |
| 5806522 | Katims | Sep 1998 | A |
| D411887 | Agarwala | Jul 1999 | S |
| 5948000 | Larsen et al. | Sep 1999 | A |
| 5954758 | Peckham et al. | Sep 1999 | A |
| 6033370 | Reinbold et al. | Mar 2000 | A |
| 6099488 | Hung | Aug 2000 | A |
| 6132387 | Gozani et al. | Oct 2000 | A |
| 6141587 | Mower | Oct 2000 | A |
| 6148233 | Owen et al. | Nov 2000 | A |
| 6161044 | Silverstone | Dec 2000 | A |
| D443362 | Storp | Jun 2001 | S |
| 6266558 | Gozani et al. | Jul 2001 | B1 |
| D450313 | Koinuma | Nov 2001 | S |
| 6430450 | Bach-y-Rita et al. | Aug 2002 | B1 |
| D462772 | Lamping et al. | Sep 2002 | S |
| 6456884 | Kenney | Sep 2002 | B1 |
| 6611789 | Darley | Aug 2003 | B1 |
| 6662051 | Eraker et al. | Dec 2003 | B1 |
| D484984 | Takizawa et al. | Jan 2004 | S |
| D534871 | Larsen | Jan 2007 | S |
| D541042 | Andre et al. | Apr 2007 | S |
| D547454 | Hsieh | Jul 2007 | S |
| D566383 | Harris et al. | Apr 2008 | S |
| D584414 | Lash et al. | Jan 2009 | S |
| D592200 | Liu | May 2009 | S |
| D598556 | Chen | Aug 2009 | S |
| D600352 | Cryan | Sep 2009 | S |
| D607198 | Andre et al. | Jan 2010 | S |
| D609353 | Cryan | Feb 2010 | S |
| 7668598 | Herregraven et al. | Feb 2010 | B2 |
| D611611 | Sachi et al. | Mar 2010 | S |
| D615526 | Andre et al. | May 2010 | S |
| 7720548 | King | May 2010 | B2 |
| 7725193 | Chu | May 2010 | B1 |
| 7787946 | Stahmann et al. | Aug 2010 | B2 |
| D625016 | Potts et al. | Oct 2010 | S |
| D625829 | Arbesman et al. | Oct 2010 | S |
| D629115 | Robertson | Dec 2010 | S |
| 7887493 | Stahmann et al. | Feb 2011 | B2 |
| D636881 | Clemens et al. | Apr 2011 | S |
| D637988 | Jinkinson | May 2011 | S |
| 8108049 | King | Jan 2012 | B2 |
| 8121702 | King | Feb 2012 | B2 |
| 8131374 | Moore et al. | Mar 2012 | B2 |
| D658302 | Nixon | Apr 2012 | S |
| 8284070 | Chaudhari et al. | Oct 2012 | B2 |
| D677792 | Vandiver | Mar 2013 | S |
| D680735 | Itabashi et al. | Apr 2013 | S |
| 8421642 | McIntosh et al. | Apr 2013 | B1 |
| D687951 | Della Torre et al. | Aug 2013 | S |
| D688707 | Vincent et al. | Aug 2013 | S |
| D704848 | Thomas et al. | May 2014 | S |
| D705428 | Cheney et al. | May 2014 | S |
| D712045 | Thornton | Aug 2014 | S |
| D712052 | Thomas et al. | Aug 2014 | S |
| D713049 | Shah | Sep 2014 | S |
| 8825175 | King | Sep 2014 | B2 |
| 8849407 | Danilov et al. | Sep 2014 | B1 |
| D716457 | Brefka et al. | Oct 2014 | S |
| 8862238 | Rahimi et al. | Oct 2014 | B2 |
| D716963 | Yosef et al. | Nov 2014 | S |
| 8948876 | Gozani et al. | Feb 2015 | B2 |
| D732682 | Porat | Jun 2015 | S |
| D735873 | Brefka et al. | Aug 2015 | S |
| 9168375 | Rahimi et al. | Oct 2015 | B2 |
| D744661 | Rizzi | Dec 2015 | S |
| D745975 | Igaue et al. | Dec 2015 | S |
| D750263 | Shigeno et al. | Feb 2016 | S |
| D750798 | Yosef et al. | Mar 2016 | S |
| 9282287 | Marsh | Mar 2016 | B1 |
| 9282897 | Ross, Jr. et al. | Mar 2016 | B2 |
| D754355 | Ganapathy et al. | Apr 2016 | S |
| D754973 | Danze et al. | May 2016 | S |
| D757292 | Chen | May 2016 | S |
| D758605 | Chen | Jun 2016 | S |
| D758606 | Chen | Jun 2016 | S |
| D759262 | Chen | Jun 2016 | S |
| D759263 | Chen | Jun 2016 | S |
| D759958 | Requa | Jun 2016 | S |
| D762628 | Yoon et al. | Aug 2016 | S |
| D762872 | Chen | Aug 2016 | S |
| D767775 | Gilmer et al. | Sep 2016 | S |
| 9452287 | Rosenbluth et al. | Sep 2016 | B2 |
| 9474898 | Gozani et al. | Oct 2016 | B2 |
| D774654 | Anderson | Dec 2016 | S |
| D775361 | Vosch et al. | Dec 2016 | S |
| D778453 | Knaus et al. | Feb 2017 | S |
| D779677 | Chen | Feb 2017 | S |
| 9561397 | Zaki | Feb 2017 | B2 |
| D784544 | Dudkiewicz et al. | Apr 2017 | S |
| D784546 | Gordon | Apr 2017 | S |
| D784946 | Jun et al. | Apr 2017 | S |
| D788056 | Choi et al. | May 2017 | S |
| 9656070 | Gozani et al. | May 2017 | B2 |
| D789546 | Matfus et al. | Jun 2017 | S |
| D789547 | Matfus et al. | Jun 2017 | S |
| D791333 | Wilson | Jul 2017 | S |
| D792363 | Kim et al. | Jul 2017 | S |
| 9700724 | Liu et al. | Jul 2017 | B2 |
| D794331 | Grote | Aug 2017 | S |
| 9731126 | Ferree et al. | Aug 2017 | B2 |
| D798170 | Toth et al. | Sep 2017 | S |
| D801542 | Anderson | Oct 2017 | S |
| D802780 | Hsu | Nov 2017 | S |
| 9827420 | Ferree et al. | Nov 2017 | B2 |
| D806669 | Kangasmaa et al. | Jan 2018 | S |
| D810311 | Chen | Feb 2018 | S |
| D810843 | Karvandi | Feb 2018 | S |
| D810952 | Hsu | Feb 2018 | S |
| D811729 | Bysshe | Mar 2018 | S |
| D813405 | Ho | Mar 2018 | S |
| D813407 | Chen | Mar 2018 | S |
| D813408 | Chen | Mar 2018 | S |
| D821592 | Pham et al. | Jun 2018 | S |
| D828569 | Mercuro | Sep 2018 | S |
| D829182 | Li | Sep 2018 | S |
| 10076662 | Tuan | Sep 2018 | B2 |
| D830565 | Xu | Oct 2018 | S |
| D831017 | Choe et al. | Oct 2018 | S |
| D831221 | Smith | Oct 2018 | S |
| D831335 | Crease | Oct 2018 | S |
| D832230 | Lee et al. | Oct 2018 | S |
| D834719 | Theriot et al. | Nov 2018 | S |
| D836788 | Peng | Dec 2018 | S |
| 10154922 | Perez et al. | Dec 2018 | B1 |
| D837394 | Cryan et al. | Jan 2019 | S |
| 10279179 | Gozani | May 2019 | B2 |
| 10335595 | Ferree et al. | Jul 2019 | B2 |
| D857910 | Cryan et al. | Aug 2019 | S |
| D861903 | Cryan et al. | Oct 2019 | S |
| D861904 | Ho | Oct 2019 | S |
| D862716 | Cryan et al. | Oct 2019 | S |
| D865986 | Cryan et al. | Nov 2019 | S |
| D879983 | Wang | Mar 2020 | S |
| 10940311 | Gozani et al. | Mar 2021 | B2 |
| 11247040 | Ferree et al. | Feb 2022 | B2 |
| 20020010497 | Merfeld et al. | Jan 2002 | A1 |
| 20030023192 | Foxlin | Jan 2003 | A1 |
| 20030074037 | Moore et al. | Apr 2003 | A1 |
| 20030114892 | Nathan et al. | Jun 2003 | A1 |
| 20030208246 | Kotlik et al. | Nov 2003 | A1 |
| 20040049241 | Campos | Mar 2004 | A1 |
| 20040122483 | Nathan et al. | Jun 2004 | A1 |
| 20040173220 | Harry et al. | Sep 2004 | A1 |
| 20050059903 | Izumi | Mar 2005 | A1 |
| 20050080463 | Stahmann et al. | Apr 2005 | A1 |
| 20050097970 | Nurse | May 2005 | A1 |
| 20050131317 | Oddsson et al. | Jun 2005 | A1 |
| 20050234525 | Phillips | Oct 2005 | A1 |
| 20050283204 | Buhlmann et al. | Dec 2005 | A1 |
| 20060052788 | Thelen et al. | Mar 2006 | A1 |
| 20060064037 | Shalon et al. | Mar 2006 | A1 |
| 20060085047 | Unsworth et al. | Apr 2006 | A1 |
| 20060085049 | Cory et al. | Apr 2006 | A1 |
| 20060089683 | Hagglof et al. | Apr 2006 | A1 |
| 20060095088 | De Ridder | May 2006 | A1 |
| 20060173507 | Mrva et al. | Aug 2006 | A1 |
| 20060190057 | Reese | Aug 2006 | A1 |
| 20060251334 | Oba et al. | Nov 2006 | A1 |
| 20070021786 | Pamis et al. | Jan 2007 | A1 |
| 20070060922 | Dreyfuss | Mar 2007 | A1 |
| 20070203435 | Novak | Aug 2007 | A1 |
| 20070203547 | Costello et al. | Aug 2007 | A1 |
| 20070276449 | Gunter et al. | Nov 2007 | A1 |
| 20080009772 | Tyler et al. | Jan 2008 | A1 |
| 20080077192 | Harry et al. | Mar 2008 | A1 |
| 20080146980 | Rousso et al. | Jun 2008 | A1 |
| 20080147143 | Popovic et al. | Jun 2008 | A1 |
| 20080147146 | Wahlgren et al. | Jun 2008 | A1 |
| 20080172102 | Shalev | Jul 2008 | A1 |
| 20080312709 | Volpe et al. | Dec 2008 | A1 |
| 20090030476 | Hargrove | Jan 2009 | A1 |
| 20090076364 | Libbus et al. | Mar 2009 | A1 |
| 20090082829 | Panken et al. | Mar 2009 | A1 |
| 20090082831 | Paul et al. | Mar 2009 | A1 |
| 20090112214 | Philippon et al. | Apr 2009 | A1 |
| 20090131993 | Rousso et al. | May 2009 | A1 |
| 20090203972 | Heneghan et al. | Aug 2009 | A1 |
| 20090240303 | Wahlstrand et al. | Sep 2009 | A1 |
| 20090264789 | Molnar et al. | Oct 2009 | A1 |
| 20090270947 | Stone et al. | Oct 2009 | A1 |
| 20090326604 | Tyler et al. | Dec 2009 | A1 |
| 20100004715 | Fahey | Jan 2010 | A1 |
| 20100010585 | Davis et al. | Jan 2010 | A1 |
| 20100042180 | Mueller et al. | Feb 2010 | A1 |
| 20100057149 | Fahey | Mar 2010 | A1 |
| 20100087903 | Van Herk et al. | Apr 2010 | A1 |
| 20100094103 | Kaplan et al. | Apr 2010 | A1 |
| 20100114257 | Torgerson | May 2010 | A1 |
| 20100131028 | Hsu et al. | May 2010 | A1 |
| 20100198124 | Bhugra | Aug 2010 | A1 |
| 20100217349 | Fahey | Aug 2010 | A1 |
| 20100241464 | Amigo et al. | Sep 2010 | A1 |
| 20100274304 | Wang et al. | Oct 2010 | A1 |
| 20110066209 | Bodlaender et al. | Mar 2011 | A1 |
| 20110106213 | Davis et al. | May 2011 | A1 |
| 20110166622 | Crosson et al. | Jul 2011 | A1 |
| 20110190594 | Heit et al. | Aug 2011 | A1 |
| 20110224665 | Crosby et al. | Sep 2011 | A1 |
| 20110245711 | Katra et al. | Oct 2011 | A1 |
| 20110257468 | Oser et al. | Oct 2011 | A1 |
| 20110264171 | Torgerson | Oct 2011 | A1 |
| 20110276107 | Simon et al. | Nov 2011 | A1 |
| 20110282164 | Yang et al. | Nov 2011 | A1 |
| 20120010680 | Wei et al. | Jan 2012 | A1 |
| 20120108998 | Molnar et al. | May 2012 | A1 |
| 20120123227 | Sun et al. | May 2012 | A1 |
| 20120130449 | Carlyon et al. | May 2012 | A1 |
| 20120303077 | De Vincentiis | Nov 2012 | A1 |
| 20120319816 | Al-Ali | Dec 2012 | A1 |
| 20130079846 | Single | Mar 2013 | A1 |
| 20130096641 | Strother et al. | Apr 2013 | A1 |
| 20130116514 | Kroner et al. | May 2013 | A1 |
| 20130158627 | Gozani et al. | Jun 2013 | A1 |
| 20130197341 | Grob et al. | Aug 2013 | A1 |
| 20130217998 | Mahfouz et al. | Aug 2013 | A1 |
| 20130317333 | Yang et al. | Nov 2013 | A1 |
| 20140005759 | Fahey et al. | Jan 2014 | A1 |
| 20140039450 | Green et al. | Feb 2014 | A1 |
| 20140057232 | Wetmore et al. | Feb 2014 | A1 |
| 20140081353 | Cook et al. | Mar 2014 | A1 |
| 20140088192 | Heller et al. | Mar 2014 | A1 |
| 20140107729 | Sumners et al. | Apr 2014 | A1 |
| 20140163444 | Ingvarsson et al. | Jun 2014 | A1 |
| 20140188194 | Schepis et al. | Jul 2014 | A1 |
| 20140206976 | Thompson et al. | Jul 2014 | A1 |
| 20140221797 | Bailey et al. | Aug 2014 | A1 |
| 20140245784 | Proud et al. | Sep 2014 | A1 |
| 20140245791 | Proud et al. | Sep 2014 | A1 |
| 20140276236 | Swain et al. | Sep 2014 | A1 |
| 20140276549 | Osorio | Sep 2014 | A1 |
| 20140296934 | Gozani et al. | Oct 2014 | A1 |
| 20140296935 | Ferree et al. | Oct 2014 | A1 |
| 20140309709 | Gozani et al. | Oct 2014 | A1 |
| 20140336725 | Nogueira | Nov 2014 | A1 |
| 20140336730 | Simon et al. | Nov 2014 | A1 |
| 20140343625 | Laighin | Nov 2014 | A1 |
| 20140371547 | Gartenberg et al. | Dec 2014 | A1 |
| 20140371814 | Spizzirri et al. | Dec 2014 | A1 |
| 20140379045 | Rahimi et al. | Dec 2014 | A1 |
| 20150012068 | Bradley et al. | Jan 2015 | A1 |
| 20150045853 | Alataris et al. | Feb 2015 | A1 |
| 20150100107 | Kiani et al. | Apr 2015 | A1 |
| 20150157242 | Sabesan | Jun 2015 | A1 |
| 20150157868 | Franke et al. | Jun 2015 | A1 |
| 20150174402 | Thomas et al. | Jun 2015 | A1 |
| 20150238094 | Lai et al. | Aug 2015 | A1 |
| 20150272511 | Najafi et al. | Oct 2015 | A1 |
| 20150306387 | Kong et al. | Oct 2015 | A1 |
| 20150321000 | Rosenbluth et al. | Nov 2015 | A1 |
| 20150328467 | Demers et al. | Nov 2015 | A1 |
| 20150335288 | Toth et al. | Nov 2015 | A1 |
| 20150335877 | Jeffery et al. | Nov 2015 | A1 |
| 20160007931 | Rubin et al. | Jan 2016 | A1 |
| 20160029891 | Lin | Feb 2016 | A1 |
| 20160113551 | Annegarn et al. | Apr 2016 | A1 |
| 20160144174 | Ferree et al. | May 2016 | A1 |
| 20160151628 | Simon et al. | Jun 2016 | A1 |
| 20160166198 | Oddsson et al. | Jun 2016 | A1 |
| 20160189371 | Krishna Rao et al. | Jun 2016 | A1 |
| 20160213924 | Coleman et al. | Jul 2016 | A1 |
| 20160235981 | Southwell et al. | Aug 2016 | A1 |
| 20160242646 | Obma | Aug 2016 | A1 |
| 20160243359 | Sharma | Aug 2016 | A1 |
| 20160250464 | Zschaeck et al. | Sep 2016 | A1 |
| 20160310730 | Martins et al. | Oct 2016 | A1 |
| 20160367823 | Cowan et al. | Dec 2016 | A1 |
| 20170043160 | Goodall et al. | Feb 2017 | A1 |
| 20170056650 | Cohen et al. | Mar 2017 | A1 |
| 20170188864 | Drury | Jul 2017 | A1 |
| 20170188872 | Hughes et al. | Jul 2017 | A1 |
| 20170209693 | An et al. | Jul 2017 | A1 |
| 20170224990 | Goldwasser et al. | Aug 2017 | A1 |
| 20170238812 | Atlas | Aug 2017 | A1 |
| 20170368345 | Kong et al. | Dec 2017 | A1 |
| 20180000685 | Maloney et al. | Jan 2018 | A1 |
| 20180028808 | Ferree et al. | Feb 2018 | A1 |
| 20180132757 | Kong et al. | May 2018 | A1 |
| 20180177996 | Gozani et al. | Jun 2018 | A1 |
| 20180345014 | Gozani et al. | Dec 2018 | A1 |
| 20190001135 | Yoo et al. | Jan 2019 | A1 |
| 20190022372 | Dar et al. | Jan 2019 | A1 |
| 20190022386 | Gozani et al. | Jan 2019 | A1 |
| 20190134393 | Wong et al. | May 2019 | A1 |
| 20200179694 | Kong et al. | Jun 2020 | A1 |
| 20200219615 | Rabin et al. | Jul 2020 | A1 |
| 20210128904 | Terekhov | May 2021 | A1 |
| 20210260374 | Gozani et al. | Aug 2021 | A1 |
| Number | Date | Country |
|---|---|---|
| 1665563 | Sep 2005 | CN |
| 1919139 | Feb 2007 | CN |
| 1926496 | Mar 2007 | CN |
| 101557788 | Oct 2009 | CN |
| 101626804 | Jan 2010 | CN |
| 102202131 | Sep 2011 | CN |
| 102355847 | Feb 2012 | CN |
| 102740919 | Oct 2012 | CN |
| 102010052710 | May 2012 | DE |
| 0971653 | Jan 2000 | EP |
| 1 985 277 | Feb 2015 | EP |
| 61-171943 | Oct 1986 | JP |
| 4-347140 | Dec 1992 | JP |
| 9-117453 | May 1997 | JP |
| 2000-167067 | Jun 2000 | JP |
| 2005-34402 | Feb 2005 | JP |
| 2005-81068 | Mar 2005 | JP |
| 2006-68300 | Mar 2006 | JP |
| 4185846 | Sep 2008 | JP |
| WO 9742999 | Nov 1997 | WO |
| WO 9964105 | Dec 1999 | WO |
| WO 03051453 | Jun 2003 | WO |
| WO 2004078132 | Sep 2004 | WO |
| WO 2007061746 | May 2007 | WO |
| WO 2008079757 | Jul 2008 | WO |
| WO 2008088985 | Jul 2008 | WO |
| WO 2009036313 | Mar 2009 | WO |
| WO 2011075179 | Jun 2011 | WO |
| WO 2011137193 | Nov 2011 | WO |
| WO 2012116407 | Sep 2012 | WO |
| WO 2013028960 | Feb 2013 | WO |
| WO 2014172381 | Oct 2014 | WO |
| WO 2015123373 | Aug 2015 | WO |
| WO 2016201366 | Dec 2016 | WO |
| WO 2018089655 | May 2018 | WO |
| WO 2020033883 | Feb 2020 | WO |
| Entry |
|---|
| Desantana, J.M. et al., Effectiveness of transcutaneous electrical nerve stimulation for treatment of hyperalgesia and pain, Curr Rheumatol Rep., 2008, vol. 10. No. 6, pp. 492-499. |
| Melzack, R. et al., Pain Mechanisms: A New Theory, Science, Nov. 19, 1965, vol. 150, No. 3699, pp. 971-979. |
| Moran, F. et al., Hypoalgesia in Response to Transcutaneous Electrical Nerve Stimulation (TENS) Depends on Stimulation Intensity, J Pain, Aug. 2011, vol. 12, No. 8, pp. 929-935. |
| Waeber, R. et al., Biosection Search with Noisy Responses, SIAM J. Control Optim., 2013, vol. 51, No. 3, pp. 2261-2279. |
| Dailey DL et al., Transcutaneous Electrical Nerve Stimulation (TENS) Reduces Pain, Fatigue, and Hyperalgesia while Restoring Central Inhibition in Primary Fibromyalgia, Pain, Nov. 2013, vol. 154, No. 11, pp. 2554-2562. |
| Gozani SN et al., Fixed-Site High-Frequency Transcutaneous Electrical Nerve Stimulation for Treatment of Chronic Low Back and Lower Extremity Pain, Journal of Pain Research, 2016, vol. 9, pp. 469-479. |
| Ossipov MH et al., Central Modulation of Pain, The Journal of Clinical Investigation, Nov. 2010, vol. 120, No. 11, pp. 3779-3787. |
| Taborri et al., A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network, Sensors, Sep. 2014, vol. 14, pp. 16212-16234. |
| Vance et al., Using TENS for pain control: the state of the evidence, Pain Management, 2014, vol. 4, No. 3, pp. 197-209. |
| Amazon, “Quell 2.0 Wearable Pain Relief Technology”, Sep. 15, 2018.http://www.amazon/com/Quell-Wearable-Pain-Relief-Technology/dp/B07DHW2MJJ/ref=cm_cr_arp_d_product_top? ie=UTF8. Shown on p. 1. (Year: 2018). |
| Amazon, “Quell Wearable Pain Relief Technology Starter Kit”, Oct. 18, 2017. http://www.amazon.com/Quell-Wearable-ReliefTechnology-Starter/dp/B075YVCLZT/ref=cm_cr_arp_d_product_top?ie=UTF8. Shown on p. 1. (Year: 2017). |
| Amft, O. et al., Sensing Muscle Activities with Body-Worn Sensors, Conference Paper, May 2006. |
| Ancoli-Israel, S. et al., The Role of Actigraphy in the Study of Sleep and Circadian Rhythms, Sleep, 2003, 26(3), p. 342-392. |
| Aurora, R. et al., The Treatment of Restless Legs Syndrome and Periodic Limb Movement Disorder in Adults—An Update for 2012: Practice Parameters with an Evidence-Based Systematic Review and Meta-Analyses, Sleep, 2012, vol. 35, No. 8, p. 1039-1062. |
| Barbarisi, Manlio et al., Pregabalin and Transcutaneous Electrical Nerve Stimulation for Postherpetic Neuralgia Treatment, The Clinical Journal of Pain, Sep. 2010;26(7):567-572. |
| Bifulco, P. et al., A Stretchable Conductive Rubber Sensor to Detect Muscle Contraction for Prosthetic Hand Control, The 6th IEEE International Conference on E-Health and Bioengineering—EHB 2017, Jun. 22-24, 2017, pp. 173-176. |
| Bjordal JM et al., Transcutaneous electrical nerve stimulation (TENS) can reduce postoperative analgesic consumption. A meta-analysis with assessment of optimal treatment parameters for postoperative pain, European Journal of Pain, 2003, vol. 7(2): 181-188. |
| Bloodworth DM et al., Comparison of stochastic vs. conventional transcutaneous electrical stimulation for pain modulation in patients with electromyographically documented radiculopathy, American Journal of Physical Medicine & Rehabilitation, 2004, vol. 83(8): 584-591. |
| Bonnet, M. et al., Recording and Scoring Leg Movements, Sleep, 1993, vol. 16, No. 8, p. 748-759. |
| Boyle, J. et al., Randomized, Placebo-Controlled Comparison of Amitriptyline, Duloxetine, and Pregabalin in Patients With Chronic Diabetic Peripheral Neuropathic Pain, Diabetes Care, 2012, vol. 35, p. 2451-2458. |
| Chandran P et al., Development of opioid tolerance with repeated transcutaneous electrical nerve stimulation administration, Pain, 2003, vol. 102: 195-201. |
| Chen CC et al., A comparison of transcutaneous electrical nerve stimulation (TENS) at 3 and 80 pulses per second on cold-pressor pain in healthy human participants, Clinical Physiology and Functioning Imaging, 2010, vol. 30(4): 260-268. |
| Chen CC et al., An investigation into the effects of frequency-modulated transcutaneous electrical nerve stimulation (TENS) on experimentally-induced pressure pain in healthy human participants, The Journal of Pain, 2009, vol. 10(10): 1029-1037. |
| Chen CC et al., Differential frequency effects of strong nonpainful transcutaneous electrical nerve stimulation on experimentally induced ischemic pain in healthy human participants, The Clinical Journal of Pain, 2011, vol. 27(5): 434-441. |
| Chen CC et al., Does the pulse frequency of transcutaneous electrical nerve stimulation (TENS) influence hypoalgesia? A systematic review of studies using experimantal pain and healthy human participants, Physiotherapy, 2008, vol. 94: 11-20. |
| Claydon LS et al., Dose-specific effects of transcutaneous electrical nerve stimulation on experimental pain, Clinical Journal of Pain, 2011, vol. 27(7): 635-647. |
| Cole, R.J. et al., Automatic Sleep/Wake Identification From Wrist Activity, Sleep, 1992, 15(5), p. 461-469. |
| Cruccu G. et al., EFNS guidelines on neurostimulation therapy for neuropathic pain, European Journal of Neurology, 2007, vol. 14: 952-970. |
| Dailey, D. et al., Transcutaneous Electrical Nerve Stimulation Reduces Movement-Evoked Pain and Fatigue: A Randomized, Controlled Trial, Arthritis & Rheumatology, May 2020, vol. 72, No. 5, pp. 824-836. |
| Davies HTO et al., Diminishing returns or appropriate treatment strategy?—an analysis of short-term outcomes after pain clinic treatment, Pain, 1997, vol. 70: 203-208. |
| Dubinsky RM et al., Assessment: Efficacy of transcutaneous electric nerve stimulation in the treatment of pain in neurologic disorders (an evidence-based review): Report of the therapeutics and technology assessment subcommittee of the american academy of neurology, Neurology, 2010, vol. 74: 173-176. |
| Fary RE et al., Monophasic electrical stimulation produces high rates of adverse skin reactions in healthy subjects, Physiotherapy Theory and Practice, 2011, vol. 27(3): 246-251. |
| Fishbain, David A. et al. Does Pain Mediate the Pain Interference with Sleep Problem in Chronic Pain? Findings from Studies for Management of Diabetic Peripheral Neuropathic Pain with Duloxetine, Journal of Pain Symptom Management, Dec. 2008; 36(6):639-647. |
| Fishbain, David A. et al., Transcutaneous Electrical Nerve Stimulation (TENS) Treatment Outcome in Long-Term Users, The Clinical Journal of Pain, Sep. 1996; 12(3):201-214. |
| Food and Drug Administration, Draft Guidance for Industry and Staff: Class II Special Controls Guidance Document: Transcutaneous Electrical Nerve Stimulator for Pain Relief, Apr. 5, 2010. |
| Garrison DW et al., Decreased activity of spontaneous and noxiously evoked dorsal horn cells during transcutaneous electrical nerve stimulation (TENS), Pain, 1994, vol. 58: 309-315. |
| Gilron, I. et al., Chronobiological Characteristics of Neuropathic Pain: Clinical Predictors of Diurnal Pain Rhythmicity, The Clinical Journal of Pain, 2013. |
| Hausdorff, J.M. et al., Gait Variability and Fall Risk in Community-Living Older Adults: A 1-Year Prospective Study, Arch Phys Med Rehabil, Aug. 2001, vol. 82, pp. 1050-1056. |
| Hori, T. et al., Skin Potential Activities and Their Regional Differences During Normal Sleep in Humans, The Japanese Journal of Physiology, 1970, vol. 20, p. 657-671. |
| Jelinek HF et al., Electric pulse frequency and magnitude of perceived sensation during electrocutaneous forearm stimulation, Arch Phys Med Rehabil, 2010, vol. 91: 1372-1382. |
| Jin DM et al., Effect of transcutaneous electrical nerve stimulation on symptomatic diabetic peripheral neuropathy: a meta-analysis of randomized controlled trials, Diabetes Research and Clinical Practice, 2010, vol. 89: 10-15. |
| Johnson MI et al., Analgesic effects of different frequencies of transcutaneous electrical nerve stimulation on cold-induced pain in normal subjects, Pain, 1989, vol. 39: 231-236. |
| Johnson MI et al., Transcutaneous Electrical Nerve Stimulation (TENS) and TENS-like devices: do they provide pain relief?, Pain Reviews, 2001, vol. 8: 7-44. |
| Johnson MI et al., Transcutaneous electrical nerve stimulation for the management of painful conditions: focus on neuropathic pain, Expert Review of Neurotherapeutics, 2011, vol. 11(5): 735-753. |
| Johnson, M.I. et al., An in-depth study of long-term users of transcutaneous electrical nerve stimulation (TENS). Implications for clinical use of TENS. Pain. Mar. 1991;44(3):221-229. |
| Kaczmarek, Kurt A. et al.. Electrotactile and Vibrotactile Displays for Sensory Substitution Systems. IEEE Trans. Biomed. Eng. Jan. 1991;38 (1):1-16. |
| Kantor G et al., The effects of selected stimulus waveforms on pulse and phase characteristics at sensory and motor thresholds, Physical Therapy, 1994, vol. 74(10): 951-962. |
| Keller, Thierry et al., Electrodes for transcutaneous (surface) electrical stimulation. J. Automatic Control, University of Belgrade. 2008; 18(2):35-45. |
| Koumans, A. J. R. et al., Electrodermal Levels and Fluctuations During Normal Sleep, Psychophysiology, 1968, 5(3), p. 300-306. |
| Kovacevic-Ristanovic, R. et al., Nonpharmacologic Treatment of Periodic Leg Movements in Sleep, Arch. Phys. Med. Rehabil., 1991, vol. 72, p. 385-389. |
| Kripke, D.F. et al., Wrist Actigraphic Scoring for Sleep Laboratory Patients: Algorithm Development, Journal of Sleep Research, 2010, 19(4), p. 612-619. |
| Law PPW et al., Optimal stimulation frequency of transcutaneous electrical nerve stimulation on people with knee osteoarthritis, J Rehabil Med, 2004, vol. 36: 220-225. |
| Leonard G et al., Deciphering the role of endogenous opioids in high-frequency TENS using low and high doses of naloxone, Pain, 2010, vol. 151: 215-219. |
| Levy et al., A comparison of two methods for measuring thermal thresholds in diabetic neuropathy, Journal of Neurology, Neurosurgery, and Psychiatry, 1989, vol. 52: 1072-1077. |
| Lopes, L. et al., Restless Legs Syndrome and Quality of Sleep in Type 2 Diabetes, Diabetes Care, 2005, vol. 28, No. 11, p. 2633-2636. |
| Lykken, D.T., Properties of Electrodes Used in Electrodermal Measurement. J. Comp. Physiol. Psychol. Oct. 1959; 52:629-634. |
| Lykken, D.T., Square-Wave Analysis of Skin Impedance. Psychophysiology. Sep. 1970; 7(2):262-275. |
| MacFarlane, T. et al., Whether the weather influences pain? Results from EpiFunD study in North West England, Rheumatology, 2010, vol. 49, pp. 1513-1520. |
| Nightingale, S., The neuropathic pain market, Nature Reviews, 2012, vol. 11, p. 101-102. |
| Okamoto-Mizuno. K. et al., Effects of thermal environment on sleep and circadlan rhythm, Journal of Physiological Anthropology, 2012, vol. 31, No. 14, pp. 1-9. |
| Oosterhof, Jan et al., Outcome of transcutaneous electrical nerve stimulation in chronic pain: short-term results of a double-blind, randomised, placebo-controlled trial. J. Headache Pain. Sep. 2006; 7 (4):196-205. |
| Oosterhof, Jan et al., The long-term outcome of transcutaneous electrical nerve stimulation in the treatment for patients with chronic pain: a randomized, placebo-controlled trial. Pain Pract. Sep. 2012; 12(7):513-522. |
| Pantaleao MA et al., Adjusting pulse amplitude during transcutaneous electrical nerve stimulation (TENS) application produces greater hypoalgesia, The Journal of Pain, 2011, vol. 12(5): 581-590. |
| Paquet, J. et al., Wake Detection Capacity of Actigraphy During Sleep, Sleep, 2007, 30(10), p. 1362-1369. |
| Pieber K et al., Electrotherapy for the treatment of painful diabetic peripheral neuropathy: a review, Journal of Rehabilitation Medicine, 2010, vol. 42: 289-295. |
| Raskin, J. et al., A Double-Blind, Randomized Multicenter Trial Comparing Duloxetine with Placebo in the Management of Diabetic Peripheral Neuropathic Pain, Pain Medicine, 2005, 6(5), p. 346-356. |
| Sadeh, A., The Role and Validity of Actigraphy in Sleep Medicine: An Update, Sleep Medicine Reviews, 2011, vol. 15, p. 259-267. |
| Sadosky, A. et al., Burden of Illness Associated with Painful Diabetic Peripheral Neuropathy Among Adults Seeking Treatment in the US: Results from a Retrospective Chart Review and Cross-Sectional Survey, Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 2013, vol. 6, p. 79-92. |
| Sano, A et al, Quantitative analysis of wrist electrodermal activity during sleep, International Journal of Psychophysiology, 2014, vol. 94, pp. 382-389. |
| Scherder, E. J. A. et al., Transcutaneous Electrical Nerve Stimulation (TENS) Improves the Rest-Activity Rhythm in Midstage Alzheimer's Disease, Behavioral Brain Research, 1999, vol. 101, p. 105-107. |
| Sheridan et al., Some Factors Influencing the Threshold of the Electrocutaneous Stimulus, Perceptual and Motor Skills, 1966, vol. 22, pp. 647-654. |
| Susi et al., Motion Mode Recognition and Step Detection Algorithms for Mobile Phone Users, Sensors, Jan. 24, 2013, vol. 13, pp. 1539-1562. |
| Timmermans, E. et al., Self-perceived weather sensitivity and joint pain in older people with osteoarthritis in six European countries: results from the European Project on OSteoArthritis (EPOSA), BMC Musculoskeletal Disorders, 2014, vol. 15, No. 66, pp. 1-11. |
| Tryon, W. W., Issues of Validity in Actigraphic Sleep Assessment, Sleep, 2004, 27(1), p. 158-165. |
| Tsai, Y et al., Impact of Subjective Sleep Quality on Glycemic Control in Type 2 Diabetes Mellitus, Family Practice, 2012, vol. 29, p. 30-35. |
| Van Boxtel, A., Skin resistance during square-wave electrical pulses of 1 to 10 mA. Med. Biol. Eng. Comput. Nov. 1977; 15(6):679-687. |
| Van Someren, E. J. W. et al., Gravitational Artefact in Frequency Spectra of Movement Acceleration: Implications for Actigraphy in Young and Elderly Subjects, Journal of Neuroscience Methods, 1996, vol. 65, p. 55-62. |
| Webster, J. B. et al., An Activity-Based Sleep Monitor System for Ambulatory Use, Sleep, 1982, 5(4), p. 389-399. |
| Zelman, D. C. et al., Sleep Impairment in Patients With Painful Diabetic Peripheral Neuropathy, The Clinical Journal of Pain, 2006, 22(8), p. 681-685. |
| Zucconi, M. et al., The official World Association of Sleep Medicine (WASM) standards for recording and scoring periodic leg movements in sleep (PLMS) and wakefulness (PLMW) developed in collaboration with a task force from the International Restless Legs Syndrome Study Group (IRLSSG), Sleep Medicine, 2006, vol. 7, p. 175-183. |
| Number | Date | Country | |
|---|---|---|---|
| 20200179694 A1 | Jun 2020 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62892627 | Aug 2019 | US | |
| 62776834 | Dec 2018 | US |
