Methods and systems for frequency adjustment to optimize charging of implantable neurostimulator

Information

  • Patent Grant
  • 11083903
  • Patent Number
    11,083,903
  • Date Filed
    Friday, January 24, 2020
    4 years ago
  • Date Issued
    Tuesday, August 10, 2021
    2 years ago
Abstract
Devices, systems, and methods for coupling with an implantable neurostimulator for delivering one or more electrical pulses to a target region within a patient's body are disclosed herein. A device, such as a charger, can include: a power source for storing electrical energy; a resonant circuit that can have a plurality of selectable natural frequencies; a driver coupled to the power source and the resonant circuit; and a processor coupled to the resonant circuit to control the natural frequency of the resonant circuit. The processor can control the natural frequency of the resonant circuit according to stored data associated with the implantable neurostimulator.
Description
FIELD OF THE INVENTION

The present invention relates to neurostimulation treatment systems and associated devices, as well as methods of treatment, implantation and configuration of such treatment systems.


BACKGROUND OF THE INVENTION

Treatments with implantable neurostimulation systems have become increasingly common in recent years. While such systems have shown promise in treating a number of conditions, effectiveness of treatment may vary considerably between patients. A number of factors may lead to the very different outcomes that patients experience, and viability of treatment can be difficult to determine before implantation. For example, stimulation systems often make use of an array of electrodes to treat one or more target nerve structures. The electrodes are often mounted together on a multi-electrode lead, and the lead implanted in tissue of the patient at a position that is intended to result in electrical coupling of the electrode to the target nerve structure, typically with at least a portion of the coupling being provided via intermediate tissues. Other approaches may also be employed, for example, with one or more electrodes attached to the skin overlying the target nerve structures, implanted in cuffs around a target nerve, or the like. Regardless, the physician will typically seek to establish an appropriate treatment protocol by varying the electrical stimulation that is applied to the electrodes.


Current stimulation electrode placement/implantation techniques and known treatment setting techniques suffer from significant disadvantages. The nerve tissue structures of different patients can be quite different, with the locations and branching of nerves that perform specific functions and/or enervate specific organs being challenging to accurately predict or identify. The electrical properties of the tissue structures surrounding a target nerve structure may also be quite different among different patients, and the neural response to stimulation may be markedly dissimilar, with an electrical stimulation pulse pattern, frequency, and/or voltage that is effective to affect a body function for one patent may impose significant pain on, or have limited effect for, another patient. Even in patients where implantation of a neurostimulation system provides effective treatment, frequent adjustments and changes to the stimulation protocol are often required before a suitable treatment program can be determined, often involving repeated office visits and significant discomfort for the patient before efficacy is achieved. While a number of complex and sophisticated lead structures and stimulation setting protocols have been implemented to seek to overcome these challenges, the variability in lead placement results, the clinician time to establish suitable stimulation signals, and the discomfort (and in cases the significant pain) that is imposed on the patient remain less than ideal. In addition, the lifetime and battery life of such devices is relatively short, such that implanted systems are routinely replaced every few years, which requires additional surgeries, patient discomfort, and significant costs to healthcare systems.


The effectiveness of such current stimulation systems relies heavily on the ability to maintain a charge in an implanted neurostimulator. This requires effective charging of the implanted neurostimulator. However, due to complexities arising from charging an implanted device, the coupling of the implanted neurostimulator and a charger is not always effective or efficient. This leads to problems such as excess heating of the implanted neurostimulator and/or of tissue surrounding the implanted neurostimulator.


The tremendous benefits of these neural stimulation therapies have not yet been fully realized. Therefore, it is desirable to provide improved neurostimulation methods, systems and devices, as well as methods for implanting and configuring such neurostimulation systems for a particular patient or condition being treated. It would be particularly helpful to provide such systems and methods so as to improve ease of use by the physician in implanting and configuring the system, as well as to improve patient comfort and alleviation of symptoms for the patient, and/or to provide improved charging features or charging methods.


BRIEF SUMMARY OF THE INVENTION

One aspect of the present disclosure relates to a charger for coupling with an implantable neurostimulator for delivering one or more electrical pulses to a target region within a patient's body. The charger includes: a power source for storing electrical energy; a resonant circuit that can be configured to have a plurality of natural frequencies; a driver coupled to the power source and the resonant circuit, which driver can power the resonant circuit; and a processor coupled to the resonant circuit to control the natural frequency of the resonant circuit. In some embodiments, the processor can control the natural frequency of the resonant circuit according to stored data associated with the implantable neurostimulator.


In some embodiments, controlling the natural frequency of the resonant circuit according to stored data associated with the implantable neurostimulator includes: receiving identifying data from the implantable neurostimulator; and retrieving characterization data, which characterization identifies a natural frequency of a resonant circuit of the implantable neurostimulator. In some embodiments, controlling the natural frequency of the resonant circuit according to stored data associated with the implantable neurostimulator includes: detecting a first natural frequency of the resonant circuit of the charger; comparing the first natural frequency of the resonant circuit of the charger to the characterization data; and changing the natural frequency of the resonant circuit of the charger from the first natural frequency to a second natural frequency. In some embodiments, detecting the first natural frequency of the resonant circuit of the charger includes at least one of: determining the driving frequency of the driver; or detecting ringing of the resonant circuit. In some embodiments, the second natural frequency corresponds to the characterization data.


In some embodiments, the resonant circuit includes: an inductor; a first capacitor coupled in series to the inductor; and a plurality of capacitors switchably coupleable to the inductor, which plurality of capacitors are each configured to be in parallel with the first capacitor when switchably coupled to the inductor. In some embodiments, the plurality of capacitors includes three capacitors. In some embodiments, each of the plurality of capacitors is switchably coupleable to the inductor via a transistor. In some embodiments, the transistor is a field effect transistor.


In some embodiments, changing the natural frequency of the resonant circuit of the charger from the first natural frequency to a second natural frequency includes: identifying a first switch configuration of the resonant circuit resulting in the first natural frequency; identifying a second switch configuration of the resonant circuit resulting in the second natural frequency; and generating a control signal to control at least one of: the opening of at least one switch in the resonant circuit to disconnect at least one of the plurality of capacitors from the inductor; or the closing of at least one switch in the resonant circuit to connect at least one of the plurality of capacitors from the inductor. In some embodiments, changing the natural frequency of the resonant circuit of the charger from the first natural frequency to a second natural frequency includes: identifying a first inductance of an inductor in the resonant circuit resulting in the first natural frequency; identifying a second inductance of the inductor in the resonant circuit resulting in the second natural frequency; and generating a control signal to change the inductance of the inductor from the first inductance to the second inductance.


In some embodiments, changing the inductance of the inductor from the first inductance to the second inductance includes changing a saturation level of a core of the inductor from a first saturation level to a second saturation level. In some embodiments, changing the saturation level of the core of the inductor from the first saturation level to the second saturation level includes changing a voltage applied the core of the inductor from a first voltage to a second voltage.


In some embodiments, the first natural frequency is repeatedly detected, and in some embodiments, the first natural frequency is periodically detected. In some embodiments, the natural frequency of the resonant circuit can be adjusted within a range of 20 Hz. In some embodiments, the natural frequency of the resonant circuit can be adjusted within a range of 10 Hz. In some embodiments, the natural frequency of the resonant circuit can be adjusted between approximately 119 Hz and 130 Hz. In some embodiments, the driver includes a class-E driver. In some embodiments, the stored data associated with the implantable neurostimulator identifies the implantable neurostimulator and/or the stored data associated with the implantable neurostimulator identifies a previously determined natural frequency of the implantable neurostimulator.


One aspect of the present disclosure relates to a neurostimulation system for delivering one or more electrical pulses to a target region within a patient's body. The neurostimulation system includes: an implantable neurostimulator including: a hermetic housing having an external surface that is implantable within a body of a patient, the housing including a ceramic transmission region; and a charging circuit that can receive power through the ceramic region of the hermetic housing. The system can include a charger for coupling with an implantable neurostimulator, the charger including: a resonant circuit, which resonant circuit is configurable to a plurality of natural frequencies; and a processor coupled to the resonant circuit to control the natural frequency of the resonant circuit according to stored data identifying a previously determined natural frequency of the charging circuit of the implantable neurostimulator.


In some embodiments, the processor can control the natural frequency of the resonant circuit according to characterization data identifying a natural frequency of the charging circuit of the implantable neurostimulator. In some embodiments, the characterization data is uniquely associated with the implantable neurostimulator, and the characterization data is stored in a database in memory at the charger. In some embodiments, the characterization data is received at the charger from the implantable neurostimulator when the charger is coupled with the implantable neurostimulator. In some embodiments, the characterization data is generated by the charger when the database does not contain characterization data for the implantable neurostimulator.


In some embodiments, generating the characterization data includes: controlling the natural frequency of the resonant circuit to iteratively cycle through a plurality of natural frequencies of the resonant circuit; iteratively receiving data from the implantable neurostimulator indicative of a level of matching between the plurality of natural frequencies of the resonant circuit and a natural frequency of the charging circuit of the implantable neurostimulator; and identifying one of the plurality of natural frequencies as the natural frequency of the charging circuit of the implantable neurostimulator.


In some embodiments, the identified one of the plurality of natural frequencies is the one of the plurality of natural frequencies that best matches the natural frequency of the charging circuit of the implantable neurostimulator. In some embodiments, the identified one of the plurality of natural frequencies is the one of the plurality of natural frequencies that best matches the natural frequency of the charging circuit of the implantable neurostimulator over a statistically significant number of iterations. In some embodiments, the identified one of the plurality of natural frequencies is a default natural frequency.


One aspect of the present disclosure relates to a method for coupling a charger with an implantable neurostimulator for delivering one or more electrical pulses to a target region within a patient's body. The method includes: powering a resonant circuit of a charger with a driver coupled to a power source, which resonant circuit is configurable to a plurality of natural frequencies; determining a first natural frequency of the resonant circuit; retrieving stored characterization data, which characterization identifies a previously determined natural frequency of a resonant circuit of the implantable neurostimulator; and changing the natural frequency of the resonant circuit from the first natural frequency to a second natural frequency with control signals generated by a processor according to the characterization data.


In some embodiments, determining the natural frequency of the resonant circuit of the charger includes at least one of: determining the driving frequency of the driver; or detecting ringing of the resonant circuit. In some embodiments, the resonant circuit includes: an inductor; a first capacitor coupled in series to the inductor; and a plurality of capacitors switchably coupleable to the inductor, which plurality of capacitors can be in parallel with the first capacitor when switchably coupled to the inductor. In some embodiments, the plurality of capacitors includes three capacitors. In some embodiments, each of the plurality of capacitors is switchably coupleable to the inductor via a transistor.


In some embodiments, changing the natural frequency of the resonant circuit of the charger from the first natural frequency to a second natural frequency includes: identifying a first switch configuration of the resonant circuit resulting in the first natural frequency; identifying a second switch configuration of the resonant circuit resulting in the second natural frequency; and generating a control signal to control at least one of: the opening of at least one switch in the resonant circuit to disconnect at least one of the plurality of capacitors from the inductor; or the closing of at least one switch in the resonant circuit to connect at least one of the plurality of capacitors from the inductor. In some embodiments, changing the natural frequency of the resonant circuit of the charger from the first natural frequency to a second natural frequency includes: identifying a first inductance of an inductor in the resonant circuit resulting in the first natural frequency; identifying a second inductance of the inductor in the resonant circuit resulting in the second natural frequency; and generating a control signal to change the inductance of the inductor from the first inductance to the second inductance.


In some embodiments, the characterization data is retrieved from a database in memory at the charger. In some embodiments, the characterization data is generated by the charger. In some embodiments, generating the characterization data includes: controlling the natural frequency of the resonant circuit to iteratively cycle through a plurality of natural frequencies of the resonant circuit; iteratively receiving data from the implantable neurostimulator indicative of a level of matching between the plurality of natural frequencies of the resonant circuit and a natural frequency of the charging circuit of the implantable neurostimulator; and identifying one of the plurality of natural frequencies as the natural frequency of the charging circuit of the implantable neurostimulator.


Further areas of applicability of the present disclosure will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description and specific examples, while indicating various embodiments, are intended for purposes of illustration only and are not intended to necessarily limit the scope of the disclosure.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 schematically illustrates a nerve stimulation system, which includes a clinician programmer and a patient remote used in positioning and/or programming of both a trial neurostimulation system and a permanently implanted neurostimulation system, in accordance with aspects of the invention.



FIGS. 2A-2C show diagrams of the nerve structures along the spine, the lower back and sacrum region, which may be stimulated in accordance with aspects of the invention.



FIG. 3A shows an example of a fully implanted neurostimulation system in accordance with aspects of the invention.



FIG. 3B shows an example of a neurostimulation system having a partly implanted stimulation lead and an external pulse generator adhered to the skin of the patient for use in a trial stimulation, in accordance with aspects of the invention.



FIG. 4 shows an example of a neurostimulation system having an implantable stimulation lead, an implantable pulse generator, and an external charging device, in accordance with aspects of the invention.



FIGS. 5A-5C show detail views of an implantable pulse generator and associated components for use in a neurostimulation system, in accordance with aspects of the invention.



FIG. 6 shows a schematic illustration of one embodiment of the architecture of the IPG.



FIG. 7 shows a schematic illustration of one embodiment of the architecture of the charging device.



FIG. 8 shows a schematic illustration of one embodiment of a charging circuit including a plurality of switchably coupleable capacitors.



FIG. 9 shows a schematic illustration of one embodiment of a charging circuit including a variable inductance inductor.



FIG. 10 is a flowchart illustrating one embodiment of a process for dynamic modulation of the natural frequency of a charging circuit.



FIG. 11 is a flowchart illustrating one embodiment of a process for characterizing an IPG.



FIG. 12 is a flowchart illustrating one embodiment of a process for dynamically controlling the natural frequency of the charging circuit of the charging device.





DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to neurostimulation treatment systems and associated devices, as well as methods of treatment, implantation/placement and configuration of such treatment systems. In one particular embodiment, the invention relates to sacral nerve stimulation treatment systems configured to treat overactive bladder (“OAB”) and relieve symptoms of bladder related dysfunction. It will be appreciated, however, that the present invention may also be utilized for any variety of neuromodulation uses, such as fecal dysfunction, the treatment of pain or other indications, such as movement or affective disorders, as will be appreciated by one of skill in the art.


I. Neurostimulation Indications


Neurostimulation (or neuromodulation as may be used interchangeably hereunder) treatment systems, such as any of those described herein, can be used to treat a variety of ailments and associated symptoms, such as acute pain disorders, movement disorders, affective disorders, as well as bladder related dysfunction. Examples of pain disorders that may be treated by neurostimulation include failed back surgery syndrome, reflex sympathetic dystrophy or complex regional pain syndrome, causalgia, arachnoiditis, and peripheral neuropathy. Movement orders include muscle paralysis, tremor, dystonia and Parkinson's disease. Affective disorders include depressions, obsessive-compulsive disorder, cluster headache, Tourette syndrome and certain types of chronic pain. Bladder related dysfunctions include but are not limited to OAB, urge incontinence, urgency-frequency, and urinary retention. OAB can include urge incontinence and urgency-frequency alone or in combination. Urge incontinence is the involuntary loss or urine associated with a sudden, strong desire to void (urgency). Urgency-frequency is the frequent, often uncontrollable urges to urinate (urgency) that often result in voiding in very small amounts (frequency). Urinary retention is the inability to empty the bladder. Neurostimulation treatments can be configured to address a particular condition by effecting neurostimulation of targeted nerve tissues relating to the sensory and/or motor control associated with that condition or associated symptom.


In one aspect, the methods and systems described herein are particularly suited for treatment of urinary and fecal dysfunctions. These conditions have been historically under-recognized and significantly underserved by the medical community. OAB is one of the most common urinary dysfunctions. It is a complex condition characterized by the presence of bothersome urinary symptoms, including urgency, frequency, nocturia and urge incontinence. It is estimated that about 33 million Americans suffer from OAB. Of the adult population, about 30% of all men and 40% of all women live with OAB symptoms.


OAB symptoms can have a significant negative impact on the psychosocial functioning and the quality of life of patients. People with OAB often restrict activities and/or develop coping strategies. Furthermore, OAB imposes a significant financial burden on individuals, their families, and healthcare organizations. The prevalence of co-morbid conditions is also significantly higher for patients with OAB than in the general population. Co-morbidities may include falls and fractures, urinary tract infections, skin infections, vulvovaginitis, cardiovascular, and central nervous system pathologies. Chronic constipation, fecal incontinence, and overlapping chronic constipation occur more frequently in patients with OAB.


Conventional treatments of OAB generally include lifestyle modifications as a first course of action. Lifestyle modifications include eliminating bladder irritants (such as caffeine) from the diet, managing fluid intake, reducing weight, stopping smoking, and managing bowel regularity. Behavioral modifications include changing voiding habits (such as bladder training and delayed voiding), training pelvic floor muscles to improve strength and control of urethral sphincter, biofeedback and techniques for urge suppression. Medications are considered a second-line treatment for OAB. These include anti-cholinergic medications (oral, transdermal patch, and gel) and oral beta-3 adrenergic agonists. However, anti-cholinergics are frequently associated with bothersome, systemic side effects including dry mouth, constipation, urinary retention, blurred vision, somnolence, and confusion. Studies have found that more than 50% of patients stop using anti-cholinergic medications within 90 days due to a lack of benefit, adverse events, or cost.


When these approaches are unsuccessful, third-line treatment options suggested by the American Urological Association include intradetrusor (bladder smooth muscle) injections of Botulinum Toxin (BoNT-A), Percutaneous Tibial Nerve Stimulation (PTNS) and Sacral Nerve Stimulation (SNM). BoNT-A (Botox®) is administered via a series of intradetrusor injections under cystoscopic guidance, but repeat injections of Botox are generally required every 4 to 12 months to maintain effect and Botox may undesirably result in urinary retention. A number of randomized controlled studies have shown some efficacy of BoNT-A in OAB patients, but long-term safety and effectiveness of BoNT-A for OAB is largely unknown.


Alternative treatment methods, typically considered when the above approaches prove ineffective, is neurostimulation of nerves relating to the urinary system. Such neurostimulation methods include PTNS and SNM. PTNS therapy consists of weekly, 30-minute sessions over a period of 12 weeks, each session using electrical stimulation that is delivered from a hand-held stimulator to the sacral plexus via the tibial nerve. For patients who respond well and continue treatment, ongoing sessions, typically every 3-4 weeks, are needed to maintain symptom reduction. There is potential for declining efficacy if patients fail to adhere to the treatment schedule. Efficacy of PTNS has been demonstrated in a few randomized-controlled studies; however, long-term safety and effectiveness of PTNS are relatively unknown at this time.


II. Sacral Neuromodulation


SNM is an established therapy that provides a safe, effective, reversible, and long-lasting treatment option for the management of urge incontinence, urgency-frequency, and non-obstructive urinary retention. SNM therapy involves the use of mild electrical pulses to stimulate the sacral nerves located in the lower back. Electrodes are placed next to a sacral nerve, usually at the S3 level, by inserting the electrode leads into the corresponding foramen of the sacrum. The electrodes are inserted subcutaneously and are subsequently attached to an implantable pulse generator (IPG), also referred to herein as an “implantable neurostimulator” or a “neurostimulator.” The safety and effectiveness of SNM for the treatment of OAB, including durability at five years for both urge incontinence and urgency-frequency patients, are supported by multiple studies and are well-documented. SNM has also been approved to treat chronic fecal incontinence in patients who have failed or are not candidates for more conservative treatments.


A. Implantation of Sacral Neuromodulation System

Currently, SNM qualification has a trial phase, and is followed if successful by a permanent implant. The trial phase is a test stimulation period where the patient is allowed to evaluate whether the therapy is effective. Typically, there are two techniques that are utilized to perform the test stimulation. The first is an office-based procedure termed the Percutaneous Nerve Evaluation (PNE) and the other is a staged trial.


In the PNE, a foramen needle is typically used first to identify the optimal stimulation location, usually at the S3 level, and to evaluate the integrity of the sacral nerves. Motor and sensory responses are used to verify correct needle placement, as described in Table 1 below. A temporary stimulation lead (a unipolar electrode) is then placed near the sacral nerve under local anesthesia. This procedure can be performed in an office setting without fluoroscopy. The temporary lead is then connected to an external pulse generator (EPG) taped onto the skin of the patient during the trial phase. The stimulation level can be adjusted to provide an optimal comfort level for the particular patient. The patient will monitor his or her voiding for 3 to 7 days to see if there is any symptom improvement. The advantage of the PNE is that it is an incision free procedure that can be performed in the physician's office using local anesthesia. The disadvantage is that the temporary lead is not securely anchored in place and has the propensity to migrate away from the nerve with physical activity and thereby cause failure of the therapy. If a patient fails this trial test, the physician may still recommend the staged trial as described below. If the PNE trial is positive, the temporary trial lead is removed and a permanent quadri-polar tined lead is implanted along with an IPG under general anesthesia.


A staged trial involves the implantation of the permanent quadri-polar tined stimulation lead into the patient from the start. It also requires the use of a foramen needle to identify the nerve and optimal stimulation location. The lead is implanted near the S3 sacral nerve and is connected to an EPG via a lead extension. This procedure is performed under fluoroscopic guidance in an operating room and under local or general anesthesia. The EPG is adjusted to provide an optimal comfort level for the patient and the patient monitors his or her voiding for up to two weeks. If the patient obtains meaningful symptom improvement, he or she is considered a suitable candidate for permanent implantation of the IPG under general anesthesia, typically in the upper buttock area, as shown in FIGS. 1 and 3A.









TABLE 1







Motor and Sensory Responses of SNM


at Different Sacral Nerve Root










Response











Nerve Innervation
Pelvic Floor
Foot/calf/leg
Sensation














S2
Primary
“clamp” of anal
Leg/hip
Contraction



somatic
sphincter
rotation,
of base of



contributor

plantar
penis,



of prudendal

flexion
vagina



nerve for

of entire foot,



external

contraction



sphincter,

of calf



leg, foot


S3
Virtually all
“bellows”**
Plantar
Pulling in



pelvic
of perineum
flexion
rectum,



autonomic

of great toe,
extending



functions and

occasionally
forward to



striated

other toes
scrotum or



muscle


labia



(levator ani)


S4
Pelvic
“bellows”
No lower
Pulling in



autonomic

extremity motor
rectum



and somatic

stimulation
only



No leg or



foot





* Clamp: contraction of anal sphincter and, in males, retraction of base of penis. Move buttocks aside and look for anterior/posterior shortening of the perineal structures.


**Bellows: lifting and dropping of pelvic floor. Look for deepening and flattening of buttock groove.






In regard to measuring outcomes for SNM treatment of voiding dysfunction, the voiding dysfunction indications (e.g., urge incontinence, urgency-frequency, and non-obstructive urinary retention) are evaluated by unique primary voiding diary variables. The therapy outcomes are measured using these same variables. SNM therapy is considered successful if a minimum of 50% improvement occurs in any of primary voiding diary variables compared with the baseline. For urge incontinence patients, these voiding diary variables may include: number of leaking episodes per day, number of heavy leaking episodes per day, and number of pads used per day. For patients with urgency-frequency, primary voiding diary variables may include: number of voids per day, volume voided per void and degree of urgency experienced before each void. For patients with retention, primary voiding diary variables may include: catheterized volume per catheterization and number of catheterizations per day.


The mechanism of action of SNM is multifactorial and impacts the neuro-axis at several different levels. In patients with OAB, it is believed that pudendal afferents can activate the inhibitory reflexes that promote bladder storage by inhibiting the afferent limb of an abnormal voiding reflex. This blocks input to the pontine micturition center, thereby restricting involuntary detrusor contractions without interfering with normal voiding patterns. For patients with urinary retention, SNM is believed to activate the pudendal nerve afferents originating from the pelvic organs into the spinal cord. At the level of the spinal cord, pudendal afferents may turn on voiding reflexes by suppressing exaggerated guarding reflexes, thus relieving symptoms of patients with urinary retention so normal voiding can be facilitated. In patients with fecal incontinence, it is hypothesized that SNM stimulates pudendal afferent somatic fibers that inhibit colonic propulsive activity and activates the internal anal sphincter, which in turn improves the symptoms of fecal incontinence patients. The present invention relates to a system adapted to deliver neurostimulation to targeted nerve tissues in a manner that disrupts, inhibits, or prevents neural activity in the targeted nerve tissues so as to provide therapeutic effect in treatment of OAB or bladder related dysfunction. In one aspect, the system is adapted to provide therapeutic effect by neurostimulation without inducing motor control of the muscles associated with OAB or bladder related dysfunction by the delivered neurostimulation. In another aspect, the system is adapted to provide such therapeutic effect by delivery of sub-threshold neurostimulation below a threshold that induces paresthesia and/or neuromuscular response or to allow adjustment of neurostimulation to delivery therapy at sub-threshold levels.


B. Positioning Neurostimulation Leads with EMG

While conventional approaches have shown efficacy in treatment of bladder related dysfunction, there exists a need to improve positioning of the neurostimulation leads and consistency between the trial and permanent implantation positions of the lead. Neurostimulation relies on consistently delivering therapeutic stimulation from a pulse generator, via one or more neurostimulation electrodes, to particular nerves or targeted regions. The neurostimulation electrodes are provided on a distal end of an implantable lead that can be advanced through a tunnel formed in patient tissue. Implantable neurostimulation systems provide patients with great freedom and mobility, but it may be easier to adjust the neurostimulation electrodes of such systems before they are surgically implanted. It is desirable for the physician to confirm that the patient has desired motor and/or sensory responses before implanting an IPG. For at least some treatments (including treatments of at least some forms of urinary and/or fecal dysfunction), demonstrating appropriate motor responses may be highly beneficial for accurate and objective lead placement while the sensory response may not be required or not available (e.g., patient is under general anesthesia).


Placement and calibration of the neurostimulation electrodes and implantable leads sufficiently close to specific nerves can be beneficial for the efficacy of treatment. Accordingly, aspects and embodiments of the present disclosure are directed to aiding and refining the accuracy and precision of neurostimulation electrode placement. Further, aspects and embodiments of the present disclosure are directed to aiding and refining protocols for setting therapeutic treatment signal parameters for a stimulation program implemented through implanted neurostimulation electrodes.


Prior to implantation of the permanent device, patients may undergo an initial testing phase to estimate potential response to treatment. As discussed above, PNE may be done under local anesthesia, using a test needle to identify the appropriate sacral nerve(s) according to a subjective sensory response by the patient. Other testing procedures can involve a two-stage surgical procedure, where a quadri-polar tined lead is implanted for a testing phase to determine if patients show a sufficient reduction in symptom frequency, and if appropriate, proceeding to the permanent surgical implantation of a neuromodulation device. For testing phases and permanent implantation, determining the location of lead placement can be dependent on subjective qualitative analysis by either or both of a patient or a physician.


In exemplary embodiments, determination of whether or not an implantable lead and neurostimulation electrode is located in a desired or correct location can be accomplished through use of electromyography (“EMG”), also known as surface electromyography. EMG is a technique that uses an EMG system or module to evaluate and record electrical activity produced by muscles, producing a record called an electromyogram. EMG detects the electrical potential generated by muscle cells when those cells are electrically or neurologically activated. The signals can be analyzed to detect activation level or recruitment order. EMG can be performed through the skin surface of a patient, intramuscularly or through electrodes disposed within a patient near target muscles, or using a combination of external and internal structures. When a muscle or nerve is stimulated by an electrode, EMG can be used to determine if the related muscle is activated, (i.e. whether the muscle fully contracts, partially contracts, or does not contract), in response to the stimulus. Accordingly, the degree of activation of a muscle can indicate whether an implantable lead or neurostimulation electrode is located in the desired or correct location on a patient. Further, the degree of activation of a muscle can indicate whether a neurostimulation electrode is providing a stimulus of sufficient strength, amplitude, frequency, or duration to affect a treatment regimen on a patient. Thus, use of EMG provides an objective and quantitative means by which to standardize placement of implantable leads and neurostimulation electrodes, reducing the subjective assessment of patient sensory responses.


In some approaches, positional titration procedures may optionally be based in part on a paresthesia or pain-based subjective response from a patient. In contrast, EMG triggers a measureable and discrete muscular reaction. As the efficacy of treatment often relies on precise placement of the neurostimulation electrodes at target tissue locations and the consistent, repeatable delivery of neurostimulation therapy, using an objective EMG measurement can substantially improve the utility and success of SNM treatment. The measureable muscular reaction can be a partial or a complete muscular contraction, including a response below the triggering of an observable motor response, such as those shown in Table 1, depending on the stimulation of the target muscle. In addition, by utilizing a trial system that allows the neurostimulation lead to remain implanted for use in the permanently implanted system, the efficacy and outcome of the permanently implanted system is more consistent with the results of the trial period, which moreover leads to improved patient outcomes.


C. Example Embodiments


FIG. 1 schematically illustrates an exemplary nerve stimulation system, which includes both a trial neurostimulation system 200 and a permanently implanted neurostimulation system 100, in accordance with aspects of the invention. The EPG 80 and IPG 10 are each compatible with and wirelessly communicate with a clinician programmer 60 and a patient remote 70, which are used in positioning and/or programming the trial neurostimulation system 200 and/or permanently implanted system 100 after a successful trial. As discussed above, the clinician programmer can include specialized software, specialized hardware, and/or both, to aid in lead placement, programming, re-programming, stimulation control, and/or parameter setting. In addition, each of the IPG and the EPG allows the patient at least some control over stimulation (e.g., initiating a pre-set program, increasing or decreasing stimulation), and/or to monitor battery status with the patient remote. This approach also allows for an almost seamless transition between the trial system and the permanent system.


In one aspect, the clinician programmer 60 is used by a physician to adjust the settings of the EPG and/or IPG while the lead is implanted within the patient. The clinician programmer can be a tablet computer used by the clinician to program the IPG, or to control the EPG during the trial period. The clinician programmer can also include capability to record stimulation-induced electromyograms to facilitate lead placement and programming. The patient remote 70 can allow the patient to turn the stimulation on or off, or to vary stimulation from the IPG while implanted, or from the EPG during the trial phase.


In another aspect, the clinician programmer 60 has a control unit which can include a microprocessor and specialized computer-code instructions for implementing methods and systems for use by a physician in deploying the treatment system and setting up treatment parameters. The clinician programmer generally includes a user interface which can be a graphical user interface, an EMG module, electrical contacts such as an EMG input that can couple to an EMG output stimulation cable, an EMG stimulation signal generator, and a stimulation power source. The stimulation cable can further be configured to couple to any or all of an access device (e.g., a foramen needle), a treatment lead of the system, or the like. The EMG input may be configured to be coupled with one or more sensory patch electrode(s) for attachment to the skin of the patient adjacent a muscle (e.g., a muscle enervated by a target nerve). Other connectors of the clinician programmer may be configured for coupling with an electrical ground or ground patch, an electrical pulse generator (e.g., an EPG or an IPG), or the like. As noted above, the clinician programmer can include a module with hardware and computer-code to execute EMG analysis, where the module can be a component of the control unit microprocessor, a pre-processing unit coupled to or in-line with the stimulation and/or sensory cables, or the like.


In some aspects, the clinician programmer is configured to operate in combination with an EPG when placing leads in a patient body. The clinician programmer can be electronically coupled to the EPG during test simulation through a specialized cable set. The test simulation cable set can connect the clinician programmer device to the EPG and allow the clinician programmer to configure, modify, or otherwise program the electrodes on the leads connected to the EPG.


The electrical pulses generated by the EPG and IPG are delivered to one or more targeted nerves via one or more neurostimulation electrodes at or near a distal end of each of one or more leads. The leads can have a variety of shapes, can be a variety of sizes, and can be made from a variety of materials, which size, shape, and materials can be tailored to the specific treatment application. While in this embodiment, the lead is of a suitable size and length to extend from the IPG and through one of the foramen of the sacrum to a targeted sacral nerve, in various other applications, the leads may be, for example, implanted in a peripheral portion of the patient's body, such as in the arms or legs, and can be configured to deliver electrical pulses to the peripheral nerve such as may be used to relieve chronic pain. It is appreciated that the leads and/or the stimulation programs may vary according to the nerves being targeted.



FIGS. 2A-2C show diagrams of various nerve structures of a patient, which may be used in neurostimulation treatments, in accordance with aspects of the invention. FIG. 2A shows the different sections of the spinal cord and the corresponding nerves within each section. The spinal cord is a long, thin bundle of nerves and support cells that extend from the brainstem along the cervical cord, through the thoracic cord and to the space between the first and second lumbar vertebra in the lumbar cord. Upon exiting the spinal cord, the nerve fibers split into multiple branches that innervate various muscles and organs transmitting impulses of sensation and control between the brain and the organs and muscles. Since certain nerves may include branches that innervate certain organs, such as the bladder, and branches that innervate certain muscles of the leg and foot, stimulation of the nerve at or near the nerve root near the spinal cord can stimulate the nerve branch that innervate the targeted organ, which may also result in muscle responses associated with the stimulation of the other nerve branch. Thus, by monitoring for certain muscle responses, such as those in Table 1, either visually, through the use of EMG as described herein or both, the physician can determine whether the targeted nerve is being stimulated. While stimulation at a certain threshold may trigger the noted muscle responses, stimulation at a sub-threshold level may still provide stimulation to the nerve associated with the targeted organ without causing the corresponding muscle response, and in some embodiments, without causing any paresthesia. This is advantageous as it allows for treatment of the condition by neurostimulation without otherwise causing patient discomfort, pain or undesired muscle responses.



FIG. 2B shows the nerves associated with the lower back section, in the lower lumbar cord region where the nerve bundles exit the spinal cord and travel through the sacral foramens of the sacrum. In some embodiments, the neurostimulation lead is advanced through the foramen until the neurostimulation electrodes are positioned at the anterior sacral nerve root, while the anchoring portion of the lead proximal of the stimulation electrodes are generally disposed dorsal of the sacral foramen through which the lead passes, so as to anchor the lead in position. FIG. 2C shows detail views of the nerves of the lumbosacral trunk and the sacral plexus, in particular, the S1-S5 nerves of the lower sacrum. The S3 sacral nerve is of particular interest for treatment of bladder-related dysfunction, and in particular OAB.



FIG. 3A schematically illustrates an example of a fully implanted neurostimulation system 100 adapted for sacral nerve stimulation. Neurostimulation system 100 includes an IPG implanted in a lower back region and connected to a neurostimulation lead extending through the S3 foramen for stimulation of the S3 sacral nerve. The lead is anchored by a tined anchor portion 30 that maintains a position of a set of neurostimulation electrodes 40 along the targeted nerve, which in this example, is the anterior sacral nerve root S3 which enervates the bladder so as to provide therapy for various bladder related dysfunctions. While this embodiment is adapted for sacral nerve stimulation, it is appreciated that similar systems can be used in treating patients with, for example, chronic, severe, refractory neuropathic pain originating from peripheral nerves or various urinary dysfunctions or still further other indications. Implantable neurostimulation systems can be used to either stimulate a target peripheral nerve or the posterior epidural space of the spine.


Properties of the electrical pulses can be controlled via a controller of the implanted pulse generator. In some embodiments, these properties can include, for example, the frequency, strength, pattern, duration, or other aspects of the electrical pulses. These properties can include, for example, a voltage, a current, or the like. This control of the electrical pulses can include the creation of one or more electrical pulse programs, plans, or patterns, and in some embodiments, this can include the selection of one or more pre-existing electrical pulse programs, plans, or patterns. In the embodiment depicted in FIG. 3A, the implantable neurostimulation system 100 includes a controller in the IPG having one or more pulse programs, plans, or patterns that may be pre-programmed or created as discussed above. In some embodiments, these same properties associated with the IPG may be used in an EPG of a partly implanted trial system used before implantation of the permanent neurostimulation system 100.



FIG. 3B shows a schematic illustration of a trial neurostimulation system 200 utilizing an EPG patch 81 adhered to the skin of a patient, particularly to the abdomen of a patient, the EPG 80 being encased within the patch. In one aspect, the lead is hardwired to the EPG, while in another the lead is removably coupled to the EPG through a port or aperture in the top surface of the flexible patch 81. Excess lead can be secured by an additional adherent patch. In one aspect, the EPG patch is disposable such that the lead can be disconnected and used in a permanently implanted system without removing the distal end of the lead from the target location. Alternatively, the entire system can be disposable and replaced with a permanent lead and IPG. When the lead of the trial system is implanted, an EMG obtained via the clinician programmer using one or more sensor patches can be used to ensure that the leads are placed at a location proximate to the target nerve or muscle, as discussed previously.


In some embodiments, the trial neurostimulation system utilizes an EPG 80 within an EPG patch 81 that is adhered to the skin of a patient and is coupled to the implanted neurostimulation lead 20 through a lead extension 22, which is coupled with the lead 20 through a connector 21. This extension and connector structure allows the lead to be extended so that the EPG patch can be placed on the abdomen and allows use of a lead having a length suitable for permanent implantation should the trial prove successful. This approach may utilize two percutaneous incisions, the connector provided in the first incision and the lead extensions extending through the second percutaneous incision, there being a short tunneling distance (e.g., about 10 cm) therebetween. This technique may also minimize movement of an implanted lead during conversion of the trial system to a permanently implanted system.


In one aspect, the EPG unit is wirelessly controlled by a patient remote and/or the clinician programmer in a similar or identical manner as the IPG of a permanently implanted system. The physician or patient may alter treatment provided by the EPG through use of such portable remotes or programmers and the treatments delivered are recorded on a memory of the programmer for use in determining a treatment suitable for use in a permanently implanted system. The clinician programmer can be used in lead placement, programming and/or stimulation control in each of the trial and permanent nerve stimulation systems. In addition, each nerve stimulation system allows the patient to control stimulation or monitor battery status with the patient remote. This configuration is advantageous as it allows for an almost seamless transition between the trial system and the permanent system. From the patient's viewpoint, the systems will operate in the same manner and be controlled in the same manner, such that the patient's subjective experience in using the trial system more closely matches what would be experienced in using the permanently implanted system. Thus, this configuration reduces any uncertainties the patient may have as to how the system will operate and be controlled such that the patient will be more likely to convert a trial system to a permanent system.


As shown in the detailed view of FIG. 3B, the EPG 80 is encased within a flexible laminated patch 81, which includes an aperture or port through which the EPG 80 is connected to the lead extension 22. The patch may further include an “on/off” button 83 with a molded tactile detail to allow the patient to turn the EPG on and/or off through the outside surface of the adherent patch 81. The underside of the patch 81 is covered with a skin-compatible adhesive 82 for continuous adhesion to a patient for the duration of the trial period. For example, a breathable strip having skin-compatible adhesive 82 would allow the EPG 80 to remain attached to the patient continuously during the trial, which may last over a week, typically two weeks to four weeks, or even longer.



FIG. 4 illustrates an example neurostimulation system 100 that is fully implantable and adapted for sacral nerve stimulation treatment. The implantable system 100 includes an IPG 10 that is coupled to a neurostimulation lead 20 that includes a group of neurostimulation electrodes 40 at a distal end of the lead. The lead includes a lead anchor portion 30 with a series of tines extending radially outward so as to anchor the lead and maintain a position of the neurostimulation lead 20 after implantation. The lead 20 may further include one or more radiopaque markers 25 to assist in locating and positioning the lead using visualization techniques such as fluoroscopy. In some embodiments, the IPG provides monopolar or bipolar electrical pulses that are delivered to the targeted nerves through one or more neurostimulation electrodes, typically four electrodes. In sacral nerve stimulation, the lead is typically implanted through the S3 foramen as described herein.


In one aspect, the IPG is rechargeable wirelessly through conductive coupling by use of a charging device 50 (CD), which is a portable device powered by a rechargeable battery to allow patient mobility while charging. The CD 50 is used for transcutaneous charging of the IPG through RF induction. The CD 50 can either be either patched to the patient's skin using an adhesive or can be held in place using a belt 53 or by an adhesive patch 52. The CD 50 may be charged by plugging the CD directly into an outlet or by placing the CD in a charging dock or station 51 that connects to an AC wall outlet or other power source.


The CD 50 can include a housing 51. The housing 51 can comprise a variety of shapes and sizes. In some embodiments, the housing 51 can be cylindrically shaped as shown in FIG. 4, and specifically, can comprise a plurality of connected cylindrical portions, wherein the connected cylindrical portions have different diameters and/or lengths. In some embodiments, the housing 51 can be a metal or polymer such as a plastic or the like.


The CD 50 can include a processor and/or memory adapted to provide instructions to and receive information from the other components of the implantable neurostimulation system. The processor can include a microprocessor, such as a commercially available microprocessor from Intel® or Advanced Micro Devices, Inc.®, or the like. The CD 50 may include an energy storage feature, such as one or more capacitors, and typically includes a wireless charging unit. Some details of CD 50 will be discussed at greater lengths below with respect to FIG. 7.


The system may further include a patient remote 70 and clinician programmer 60, each configured to wirelessly communicate with the implanted IPG, or with the EPG during a trial. The clinician programmer 60 may be a tablet computer used by the clinician to program the IPG and the EPG. The device also has the capability to record stimulation-induced electromyograms (EMGs) to facilitate lead placement, programming, and/or re-programming. The patient remote may be a battery-operated, portable device that utilizes radio-frequency (RF) signals to communicate with the EPG and IPG and allows the patient to adjust the stimulation levels, check the status of the IPG battery level, and/or to turn the stimulation on or off.



FIG. 5A-5C show detail views of the IPG and its internal components. In some embodiments, the pulse generator can generate one or more non-ablative electrical pulses that are delivered to a nerve to control pain or cause some other desired effect, for example to inhibit, prevent, or disrupt neural activity for the treatment of OAB or bladder related dysfunction. In some applications, the pulses having a pulse amplitude in a range between 0 mA to 1,000 mA, 0 mA to 100 mA, 0 mA to 50 mA, 0 mA to 25 mA, and/or any other or intermediate range of amplitudes may be used. One or more of the pulse generators can include a processor and/or memory adapted to provide instructions to and receive information from the other components of the implantable neurostimulation system. The processor can include a microprocessor, such as a commercially available microprocessor from Intel® or Advanced Micro Devices, Inc.®, or the like. An IPG may include an energy storage feature, such as one or more capacitors, and typically includes a wireless charging unit.


One or more properties of the electrical pulses can be controlled via a controller of the IPG or EPG. In some embodiments, these properties can include, for example, the frequency, strength, pattern, duration, or other aspects of the timing and magnitude of the electrical pulses. These properties can further include, for example, a voltage, a current, or the like. This control of the electrical pulses can include the creation of one or more electrical pulse programs, plans, or patterns, and in some embodiments, this can include the selection of one or more pre-existing electrical pulse programs, plans, or patterns. In one aspect, the IPG 100 includes a controller having one or more pulse programs, plans, or patterns that may be created and/or pre-programmed. In some embodiments, the IPG can be programmed to vary stimulation parameters including pulse amplitude in a range from 0 mA to 10 mA, pulse width in a range from 50 μs to 500 μs, pulse frequency in a range from 5 Hz to 250 Hz, stimulation modes (e.g., continuous or cycling), and electrode configuration (e.g., anode, cathode, or off), to achieve the optimal therapeutic outcome specific to the patient. In particular, this allows for an optimal setting to be determined for each patient even though each parameter may vary from person to person.


As shown in FIGS. 5A-5B, the IPG may include a header portion 11 at one end and a ceramic portion 14 at the opposite end. The header portion 11 houses a feed-through assembly 12 and connector stack 13, while the ceramic case portion 14 houses an antennae assembly 16 to facilitate wireless communication with the clinician program, the patient remote, and/or a charging coil to facilitate wireless charging with the CD. The remainder of the IPG is covered with a titanium case portion 17, which encases the printed circuit board, memory and controller components that facilitate the electrical pulse programs described above. The ceramic portion 14 includes an end 22, sides 24, and a connection portion 26 that connects the ceramic portion 14 to the case portion 17. In the example shown in FIG. 5B, the antennae assembly 16 is positioned such that a plane 28, in which loops of a radiating element lay, is perpendicular to and extends through the sides 24 of the ceramic portion 14.


In the example shown in FIG. 5C, the header portion of the IPG includes a four-pin feed-through assembly 12 that couples with the connector stack 13 in which the proximal end of the lead is coupled. The four pins correspond to the four electrodes of the neurostimulation lead. In some embodiments, a Balseal® connector block is electrically connected to four platinum/iridium alloy feed-through pins which are brazed to an alumina ceramic insulator plate along with a titanium alloy flange. This feed-through assembly is laser seam welded to a titanium-ceramic brazed case to form a complete hermetic housing for the electronics. In some embodiments, some or all of the pieces of the IPG 10 forming the hermetic housing can be biocompatible, and specifically, can have external surfaces made of biocompatible materials.


In some embodiments, such as that shown in FIG. 5A, the ceramic and titanium brazed case is utilized on one end of the IPG where the ferrite coil and PCB antenna assemblies are positioned. A reliable hermetic seal is provided via a ceramic-to-metal brazing technique. The zirconia ceramic may comprise a 3Y-TZP (3 mol percent Yttria-stabilized tetragonal Zirconia Polycrystals) ceramic, which has a high flexural strength and impact resistance and has been commercially utilized in a number of implantable medical technologies. It will be appreciated, however, that other ceramics or other suitable materials may be used for construction of the IPG, and that ceramic may be used to form additional portions of the case.


In one aspect, utilization of ceramic material provides an efficient, radio-frequency-transparent window for wireless communication with the external patient remote and clinician's programmer as the communication antenna is housed inside the hermetic ceramic case. This ceramic window has further facilitated miniaturization of the implant while maintaining an efficient, radio-frequency-transparent window for long term and reliable wireless communication between the IPG and external controllers, such as the patient remote and clinician programmer. The IPG's wireless communication is generally stable over the lifetime of the device, unlike prior art products where the communication antenna is placed in the header outside the hermetic case. The communication reliability of such prior art devices tends to degrade due to the change in dielectric constant of the header material in the human body over time.


In another aspect, the ferrite core is part of the charging coil assembly 15, shown in FIG. 5B, which is positioned inside the ceramic case 14. The ferrite core concentrates the magnetic field flux through the ceramic case as opposed to the metallic case portion 17. This configuration maximizes coupling efficiency, which reduces the required magnetic field and in turn reduces device heating during charging. In particular, because the magnetic field flux is oriented in a direction perpendicular to the smallest metallic cross section area, heating during charging is minimized. This configuration also allows the IPG to be effectively charged at a depth of 3 cm with the CD, when positioned on a skin surface of the patient near the IPG, and reduces re-charging time.



FIG. 6 shows a schematic illustration of one embodiment of the architecture of the IPG 10. In some embodiments, each of the components of the architecture of the IPG 10 can be implemented using the processor, memory, and/or other hardware component of the IPG 10. In some embodiments, the components of the architecture of the IPG 10 can include software that interacts with the hardware of the IPG 10 to achieve a desired outcome, and the components of the architecture of the IPG 10 can be located within the housing.


In some embodiments, the IPG 10 can include, for example, a communication module 600. The communication module 600 can be configured to send data to and receive data from other components and/or devices of the exemplary nerve stimulation system including, for example, the clinician programmer 60, the charging device 50, and/or the patient remote 70. In some embodiments, the communication module 600 can include one or several antennas and software configured to control the one or several antennas to send information to and receive information from one or several of the other components of the IPG 10. In some embodiments, for example, when connecting with the charging device 50, the communications module 600 can be configured to send data identifying the IPG 10 and/or characterizing one or several attributes of the IPG 10. In some embodiments, this information can be, for example, a number uniquely identifying the IPG 10 such as, for example, a serial number, or the like. In some embodiments, this data can characterize one or several attributes of the IPG 10 such as, for example, the natural frequency of a charging module 606 of the IPG 10 and/or of one or several components of the charging module 606 of the IPG.


The IPG 10 can further include a data module 602. The data module 602 can be configured to manage data relating to the identity and properties of the IPG 10. In some embodiments, the data module can include one or several databases that can, for example, include information relating to the IPG 10 such as, for example, the identification of the IPG 10, one or several properties of the IPG 10, or the like. In one embodiment, the data identifying the IPG 10 can include, for example, a serial number of the IPG 10 and/or other identifier of the IPG 10 including, for example, a unique identifier of the IPG 10. In some embodiments, the information associated with the property of the IPG 10 can include, for example, data identifying the function of the IPG 10, data identifying the power consumption of the IPG 10, data identifying the charge capacity of the IPG 10 and/or power storage capacity of the IPG 10, data identifying potential and/or maximum rates of charging of the IPG 10, and/or the like. In some embodiments, the information associated with the property of the IPG 10 can include, for example, data identifying the natural frequency of the IPG 10 and/or components thereof. In some embodiments, this information identifying the natural frequency can be generated at the time of the manufacture of the IPG 10.


The IPG 10 can include a pulse control 604. In some embodiments, the pulse control 604 can be configured to control the generation of one or several pulses by the IPG 10. In some embodiments, for example, this can be performed based on information that identifies one or several pulse patterns, programs, or the like. This information can further specify, for example, the frequency of pulses generated by the IPG 10, the duration of pulses generated by the IPG 10, the strength and/or magnitude of pulses generated by the IPG 10, or any other details relating to the creation of one or several pulses by the IPG 10. In some embodiments, this information can specify aspects of a pulse pattern and/or pulse program, such as, for example, the duration of the pulse pattern and/or pulse program, and/or the like. In some embodiments, information relating to and/or for controlling the pulse generation of the IPG 10 can be stored within the memory.


The IPG 10 can include a charging module 606. In some embodiments, the charging module 606 can be configured to control and/or monitor the charging/recharging of the IPG 10. In some embodiments, for example, the charging module 606 can include one or several features configured to receive energy for recharging the IPG 10 such as, for example, one or several inductive coils/features that can interact with one or several inductive coils/features of the charging device 50 to create an inductive coupling to thereby recharge the IPG 10. In some embodiments, the charging module 606 can include hardware and/or software configured to monitor the charging of the IPG 10 including, for example, the charging coil assembly 15.


The charging module 606 of the IPG 10 can include a charging circuit 607, also referred to herein as the resonant circuit 607, the secondary charging circuit 607, the secondary resonant circuit 607, the receiving charging circuit 607, or the receiving resonant circuit 607. In some embodiments, the charging circuit 607 can comprise, for example, at least one of: an inductor; a capacitor; or a resistor. The charging circuit 607 can be characterized by a natural frequency, which natural frequency can be determined at, for example, the time of assembly of the charging circuit 607 or after the implantation of the IPG 10 in the body. In some embodiments, because of the relatively constant temperature and environment in the body, the natural frequency of the charging circuit 607 can remain constant after the implantation of the IPG 10 into the body.


The IPG 10 can include an energy storage device 608. The energy storage device 608, which can include the energy storage features, can be any device configured to store energy and can include, for example, one or several batteries, capacitors, fuel cells, or the like. In some embodiments, the energy storage device 608 can be configured to receive charging energy from the charging module 606.



FIG. 7 shows a schematic illustration of one embodiment of the architecture of the charging device 50. In some embodiments, each of the components of the architecture of the charging device 50 can be implemented using the processor, memory, and/or other hardware component of the charging device 50. In some embodiments, the components of the architecture of the charging device 50 can include software that interacts with the hardware of the charging device 50 to achieve a desired outcome, and the components of the architecture of the charging device 50 can be located within the housing 51.


In some embodiments, charging device 50 can include, for example, a communication module 600. The communication module 700 can be configured to send data to and receive data from other components and/or devices of the exemplary nerve stimulation system including, for example, the clinician programmer 60, the IPG 10, and/or the patient remote 70. In some embodiments, the communication module 700 can include one or several antennas and software configured to control the one or several antennas to send information to and receive information from one or several of the other components of the CD 50. In some embodiments, for example, when connecting with the IPG 10, the communications module 700 can be configured to receive data identifying the IPG 10 and/or characterizing one or several attributes of the IPG 10. In some embodiments, this information can be, for example, a number uniquely identifying the IPG 10 such as, for example, a serial number, or the like. In some embodiments, this data can characterize one or several attributes of the IPG 10 such as, for example, the natural frequency of the charging module 606 of the IPG 10 and/or of one or several components of the charging module 606 of the IPG.


The CD 50 can further include a data module 702. The data module 702 can be configured to manage data relating to the identity and properties of the IPG 10. In some embodiments, the data module can include one or several database that can, for example, include information relating to the IPG 10 such as, for example, the identification of the IPG 10, one or several properties of the IPG 10, or the like. In one embodiment, for example, the data module can comprise a database including one or several IPG 10 identifiers such as serial numbers for those one or several IPGs 10. In some embodiments, the data module 702 can further include characterization data associated with some or all of the one or several IPGs 10 identified in the data module 702. In some embodiments, for example, this characterization data can include the identification of the natural frequency of charging circuit 607 of the IPG 10. In some embodiments, this characterization data can be received from the IPG 10 and/or can be generated by the CD 50 in response to interactions with the IPG 10.


The CD 50 can include a charging module 704. In some embodiments, the charging module 704 can be configured to control and/or monitor the charging/recharging of the IPG 10. In some embodiments, for example, the charging module 704 can include one or several features configured to provide energy for recharging the IPG 10 such as, for example, one or several inductive coils/features that can interact with one or several inductive coils/features of the IPG 10 to create an inductive coupling to thereby recharge the IPG 10. In some embodiments, the charging module 704 can include hardware and/or software configured to monitor the charging of the IPG 10 including, for example, the charging coil assembly 15.


The charging module 704 of the CD 50 can include a charging circuit 706, also referred to herein as the resonant circuit 706, the primary charging circuit 706, the primary resonant circuit 706, the transmitter charging circuit 706, or the transmitter resonant circuit 706. In some embodiments, the charging circuit 706 can comprise, for example, at least one of: an inductor; a capacitor; or a resistor. The charging circuit 607 can be characterized by a natural frequency, which natural frequency can be fixed or variable. In some embodiments, the natural frequency of the charging circuit 706 can be varied by the selective switched connection of one or several capacitors to the charging circuit 706. In some embodiments, the natural frequency of the charging circuit 706 can be varied by changing the inductance of one or several inductors in the charging circuit 706. In some embodiments, the inductance of these one or several inductors can be changed by changing the electrical saturation of a core, such as a ferritic core of the inductors via, for example, the changing of a voltage or a current, such as a DC current, passing through the core of the inductor.


In some embodiments, the charging module 704 can include a driver 708. The driver 708 can be, for example, a non-class E driver, and in some embodiments, the driver 708 can be a class E driver, and specifically can be a microprocessor controlled class E driver as disclosed in U.S. patent application Ser. No. 14/446,294, filed on Jul. 29, 2014, the entirety of which is hereby incorporated by reference herein. In some embodiments, the driver 708 can be configured to provide electrical pulses to the resonant circuit 706 to thereby charge the IPG 10. In some embodiments, the driver 708 can be further configured to provide these pulses at a frequency corresponding to the natural frequency of the resonant circuit 706. Thus, in some embodiments, the natural frequency of the resonant circuit 706 of the CD 50 can be determined by determining the frequency with which the driver 708 is providing pulses to the resonant circuit 706.


The CD 50 can include an energy storage device 710. The energy storage device 710 can be any device and/or features configured to store energy and can include, for example, one or several batteries, capacitors, fuel cells, or the like. In some embodiments, the energy storage device 710 can be configured to provide charging energy to the charging module 704 for charging of the IPG 10.



FIG. 8 is a schematic illustration of one embodiment of the charging circuit 706 of the charging module 704 of the CD 50. As seen in FIG. 8, the charging circuit 706 is connected to the driver 708. The charging circuit 706 includes an inductor 800 in parallel with an optional first capacitor 802, and the inductor 800 is in series with a second capacitor 804. In some embodiments, the first capacitor 802 can be included in the charging circuit 706, for example, when the driver 708 comprises a class E driver. The charging circuit can further include ground 820.


The charging circuit 706 additionally includes a capacitor array 806 comprising a plurality of capacitors. In some embodiments, the plurality of capacitors forming the array 806 are switchably connectable or coupleable with the charging circuit 706 such that each of the capacitors of the array 806 is electrically coupled or connected to charging circuit 706 when a switch associated with each capacitor of the array 806 is closed. Similarly, in some embodiments, the plurality of capacitors forming the array 806 are switchably connectable with the charging circuit 706 such that each of the capacitors of the array 806 is electrically disconnected to charging circuit 706 when a switch associated with each capacitor of the array 806 is open.


The array 806 can be positioned such that when the capacitors in the array 806 are electrically connected to the charging circuit 706, these capacitors are in parallel with the second capacitor 804 and are in series with the inductor 800. Through this arrangement, the selective coupling of the capacitors of the array 806 can change the natural frequency of the charging circuit 706.


The array 806 can include, for example, a first array capacitor 808 switchably coupleable to the charging circuit 706 via a first switch 810, a second array capacitor 812 switchably coupleable to the charging circuit 706 via a second switch 814, and a third array capacitor 816 switchably coupleable to the charging circuit 706 via a third switch 818. In some embodiments, the switches 810, 814, 818 can be controllably connected to the processor of the charger 50 such that control signals generated by the processor can result in the opening and/or closing of one or several of the switches 810, 814, 818. In some embodiments, these switches 810, 814, 818 can each comprise one or several transistors such as field-effect transistors (FET) such as, for example, a metal-oxide-semiconductor field-effect transistor (MOSFET).


Although the embodiment of FIG. 8 depicts three array capacitors 808, 812, 816, the array 806 can include more or fewer capacitors than shown in FIG. 8. In some embodiments, the number of capacitors can be selected to provide, for example, a desired number of different achievable natural frequencies of the charging circuit. In some embodiments, this desired number of different achievable natural frequencies can be selected based on, for example, manufacturing tolerances, range of expected frequency shifts from implantation, or the like.


The array capacitors 808, 812, 816 can comprise a variety of capacitances. In some embodiments, the capacitance of the array capacitors 808, 812, 816 can be selected to provide a desired range of possible natural frequencies of the charging circuit 706. In some embodiments, for example, the array 806 can be configured to allow the natural frequency of the charging circuit 706 to be adjusted within a range of approximately 50 Hz, approximately 30 Hz, approximately 20 Hz, approximately 10 Hz, approximately 5 Hz, between 0 and 10 Hz, between 0 and 20 Hz, between 0 and 30 Hz, between 0 and 50 Hz, and/or any other or intermediate range of frequencies. As used herein, “approximately” refers to a range of values extending +/−10 percent of the associated value around that associated value. In some embodiments, for example, the array 806 can allow adjustment of the natural frequency of the charging circuit 706 between 100 Hz and 150 Hz, between 110 Hz and 140 Hz, between 119 Hz and 130 Hz, between 120 Hz and 130 Hz, and/or between any other or intermediate frequencies.


In some embodiments, the array capacitors can be selected to, for example, provide a plurality of equal and/or approximately equal changes in the natural frequencies of the charging circuit 706. In one embodiment, for example, in which the array 806 includes three array capacitors 808, 812, 816, the capacitances of these three array capacitors 808, 812, 816 can be selected to provide approximately eight equal separated natural frequencies of the charging circuit. Thus, in embodiments in which the array 806 provides approximately 10 Hz of natural frequency adjustment to the charging circuit 706, the capacitances can be selected such that each of these eight natural frequencies is separated by a step of between 1 Hz and 2 Hz or of approximately 1.25 Hz from its one or several adjacent natural frequencies.



FIG. 9 is a schematic illustration of another embodiment of the charging circuit 706 of the charging module 704 of the CD 50. As seen in FIG. 9, the charging circuit 706 is connected to the driver 708. The charging circuit 706 includes an inductor 800 in parallel with an optional first capacitor 802, and the inductor 800 is in series with a second capacitor 804. In some embodiments, the first capacitor 802 can be included in the charging circuit 706, for example, when the driver 708 comprises a class E driver. The charging circuit 706 can further include ground 820.


The charging circuit 706 can further include a second inductor 900. In some embodiments, the second inductor 900 can be positioned in series with the inductor 800. The second inductor 900 can be electrically coupled to a current source 902 and/or voltage source. In some embodiments, the current source 902 and/or the voltage source can be controlled to apply a different current and/or voltage to second inductor 902 to change the inductance of the second inductor 902 to thereby affect the natural frequency of the resonant circuit 706. In some embodiments, this change in voltage and/or current applied to the second inductor 900 can result in a change in the saturation level of the core of the second inductor 900, which core can be a ferritic core. In some embodiments, this change in the saturation of the core of the second inductor 900 can change the natural frequency of the resonant circuit 706 from a first natural frequency to a second natural frequency.



FIG. 10 is a flowchart illustrating one embodiment of a process 1000 for dynamic modulation of the natural frequency of the charging circuit 706 of the CD 50 for management of wireless charging. The process 1000 can be performed by the CD 50. The process 1000 begins at block 1002 wherein IPG data is received. The IPG data can be received by the CD 50 via the communication module 700 of the CD 50 from the communication module 600 of the IPG 10. In some embodiments, the IPG data can include an identifier and can identify the IPG 10, and can, in some embodiments, uniquely identify the IPG 10. In some embodiments, the IPG data can further include characterization data characterizing, for example, the natural frequency of the charging circuit 607 of the IPG 10.


After the IPG data has been received, the process 1000 proceeds to block 1004 wherein the IPG is identified. In some embodiments, the identification of the IPG can include the extraction of identification information from the IPG data. In some embodiments, this can be performed by a processor of the CD 50 and can be performed by, for example, a processor associated with the data module 702.


After the IPG 10 has been identified, the process 1000 proceeds to decision state 1006 wherein it is determined if there is stored data for the identified IPG 10. In some embodiments, this story data can include, for example, characterization data characterizing the natural frequency of the charging circuit 607 of the IPG 10. In some embodiments, the determination of whether there is stored data can include querying the data module 702 and/or one or several databases of the data module for information relating to the IPG 10 identified at block 1004.


If the CD 50 does not include stored data relating to that IPG 10 identified and block 1004, the process 1000 proceeds to block 1008 wherein the IPG 10 is characterized, and specifically wherein a natural frequency of the charging circuit 607 of the IPG 10 is characterized. In some embodiments, this characterization can include the selective modulation of the natural frequency of the charging circuit 706 of the CD 50. In some embodiments, this selective modulation can be iteratively performed. In some embodiments, information relating to the effectiveness of the coupling between the charging circuit 706 of the CD 50 and the charging circuit 607 of the IPG 10 can be generated by the CD 50 and/or can be received from the IPG 10. Based on this information and, in some embodiments, the number of iterations of the selective modulation of the natural frequency of the charging circuit 706 of the CD 50, a natural frequency of the IPG 10 can be identified.


In some embodiments, the IPG 10 can be characterized at the time of assembly at, for example, the factory in which the IPG 10 is assembled. In such embodiments, the IPG 10 can provide data identifying the IPG 10, and/or identifying the natural frequency of the IPG 10 to the CD 50. In some embodiments, the IPG 10 can be characterized after implantation into the body of the patient. In some embodiments, the IPG 10 can be characterized multiple times after implantation into the body of the patient, and in some embodiments, the IPG 10 can be characterized a single time after implantation. In some embodiments, for example, the natural frequency of the IPG 10 can remain constant after the implantation of the IPG 10 into the body of the patient, and thus a one-time characterization of the IPG 10 after implantation can be adequate for coupling between the CD 50 and the IPG 10.


After the IPG 10 has been characterized, and specifically after the natural frequency of the IPG 10 has been characterized, the process 1000 proceeds to block 1010 wherein the natural frequency of the IPG 10, and specifically of the charging circuit 607 of the IPG 10 is stored. In some embodiments, this natural frequency can be stored in the data module 702 of the CD 50, and more specifically can be stored in the database in the data module 702 of the CD 50.


Returning again to decision state 1006, if it is determined that the CD 50 has stored data for the IPG 10 identified at block 1004, then the process 1000 proceeds to block 1012 wherein the stored data is retrieved. In some embodiments, the store data can be retrieved from the data module 702, and particularly from one of the databases of the data module 702.


After the stored data has been retrieved, or returning to block 1010 after the IPG frequency has been stored, the process 1000 proceeds to block 1014 wherein the resonant frequency of the charging circuit is configured. In some embodiments, this can include changing and/or modifying the natural frequency of the charging circuit such that the natural frequency of the charging circuit matches and/or corresponds to the natural frequency of the IPG 10 either as characterized in block 1008 or as identified in the retrieved stored data of block 1012.


After the resonant frequency of the charging circuit has been configured, the process 1000 proceeds to block 1016 wherein the IPG 10 is charged. In some embodiments, the IPG 10 can be charged by the delivery of power to the charging circuit by the driver 708.


With reference now to FIG. 11, a flowchart illustrating one embodiment of a process 1100 for characterizing an IP 10 is shown. The process 1100 can be performed as part of, or in the place of the step depicted by block 1008 of FIG. 10. The process begins at block 1102 wherein a frequency is selected. In some embodiments, this specifically includes a selection of a natural frequency for the charging circuit 706 of the CD 50. In some embodiments, this frequency can be selected by, for example, selecting a switching configuration to electrically couple a desired number of array capacitors 808, 812, 816 to the charging circuit 706, and generating one or several control signals to cause switching corresponding to the selected switching configuration. Alternatively, this frequency can be selected by, for example, selecting an inductance of an inductor of the charging circuit 706 and generating one or several control signals to achieve the selected inductance. This selected frequency can be one of a plurality of natural frequencies of the charging circuit 706.


After the frequency has been selected, the process 1100 proceeds to block 1104 wherein the resonant frequency of the charging circuit 706 of the CD 50 is configured to match the selected frequency of block 1102. In some embodiments, this can include, for example, providing the control signals generated in block 1102 to components of the charging circuit 706 of the CD 50. In some embodiments, the components, such as, for example, switches 810, 814, 818 and/or the second inductor 900, can receive the control signals and can act according to those receipts control signals.


After the resonant frequency of the charging circuit has been configured to match the frequency selected in block 1102, the process 1100 proceeds to block 1106 wherein the IPG 10 is charged. In some embodiments, the charging of the IPG can include the control of the driver 708 to send one or several electrical pulses to the charging circuit 706 to cause resonance within the charging circuit 706 to thereby be inductively coupled with the charging circuit 607 of the IPG 10. In some embodiments, the driver 708 can provide one or several electrical pulses to the charging circuit 706 of the CD 50 at a frequency corresponding to the resonant frequency of the charging circuit 706 of the CD 50.


After or during the charging of the IPG, the process 1100 can proceed to block 1108 wherein charging efficiency feedback is received and/or generated. In some embodiments, this feedback can indicate the effectiveness of the inductive coupling between the charging circuits 607, 706 which can likewise indicate the degree to which the natural frequency of the charging circuit 706 of the CD 50 matches and/or corresponds to the natural frequency of the charging circuit 607 of the IPG 10. In some embodiments, this feedback can include and/or can be based on data indicative of the amount of power received at the IPG 10 and/or the amount of power provided to the charging circuit 706 of the CD 50. In some embodiments, this feedback can be received by the communication module 700 of the CD 50 from the communication module 600 of the IPG 10.


After the charging efficiency feedback has been received, the process 1100 proceeds to decision state 1110 wherein it is determined if the characterization cycle is complete. In some embodiments, the characterization cycle can be complete when the charging circuit 706 of the CD 50 has been configured at a desired number of natural frequencies and charging of the IPG 10 has been performed at each of those natural frequencies. In some embodiments, the cycle is completed when, the charging circuit 706 of the CD 50 is iteratively configured a desired number of times at each of the desired number of natural frequencies and the IPG is charged at each of the desired number of natural frequencies a desired number of times. If it is determined that the cycle is not complete such as, for example, when configuration of the charging circuit 706 and the desired number frequencies has not been completed and/or when the desired number of iterations has not been attained, the process 1100 returns to block 1102 and proceeds as outlined above.


If it is determined that the cycle is complete, then the process 1100 proceeds to block 1112 wherein the best performing frequency of the CD 50 is selected. In some embodiments, this best-performing's frequency can be selected based on the feedback received in block 1108. In some embodiments, the best-performing frequency of the CD 50 can be the natural frequency of the charging circuit 706 of the CD 50 that most efficiently inductively coupled with the charging circuit 607 of the IPG 10. In some embodiments, the identified natural frequency can be one of a plurality of natural frequencies. In some embodiments, the identified matching frequency is the one of the plurality of natural frequencies that best matches the natural frequency of the charging circuit 607 of the IPG 10.


After the matching frequency has been identified, the process 1100 proceeds to decision state 1114 wherein it is determined if a desired confidence level is achieved. In some embodiments, this can include applying one or several statistical methods or techniques to the IPG feedback received in block 1108. In some embodiments, these can be used to determine the statistical confidence that the matching frequency identified and block 1112 is in fact the frequency of the CD 50 that best matches the natural frequency of the IPG 10. This determination can be performed by, for example, the data module 702 or the charging module 704 of the CD 50. In some embodiments, this can include determining if the identified matching frequency matches the natural frequency of the charging circuit 607 of the IPG 10 over a statistically significant number of iterations.


If it is determined that the confidence level is not attained, then the process 1100 proceeds to block 1116 wherein it is determined if an additional cycle should be performed. In some embodiments, the additional cycle can include one or several additional iterations of configuring the charging circuit 706 of the CD 50 at one or several selected natural frequencies and charging the IPG 10 at those one or several selected natural frequencies. In some embodiments, the one or several selected natural frequencies of the additional cycle can be the same selected natural frequencies applied in previous cycles or one or several different natural frequencies that have been selected and applied in previous cycles. In some embodiments, determining whether to perform an additional cycle can include comparing the number of already completed and/or performed cycles to a maximum number of allowable cycles and determining if the number of already performed cycles meets or exceeds the maximum number of allowable cycles. Alternatively, in some embodiments, the determination of whether to perform an additional cycle can include determining whether convergence in the feedback and/or identified matching frequencies is occurring such that one or several additional cycles would likely result in the identification of a matching frequency having a desired confidence level. This determination of whether to perform an additional cycle can be made by the processor of the CD 50, and specifically by one or more of the data module 702 in the charging module 706 of the CD 50.


If it is determined to perform an additional cycle, then the process 1100 returns to block 1102 and proceeds as outlined above. If it is determined not to perform an additional cycle, then the process 1100 proceeds to block 1118 wherein a default frequency is identified. In some embodiments, the default frequency can be a natural frequency of the charging circuit 706 of the CD that can be selected when no matching frequency achieving the desired confidence level is identified. In some embodiments, one or several default frequencies can be stored in the data module 702 and the identification of a default frequency as indicated in block 1118 can include retrieving one or several default frequencies from the data module 702.


After the default frequency has been identified or, returning again to decision state 1114, if it is determined that the desired confidence level is achieved, then the process 1100 proceeds to block 1120 and continues to block 1010 of FIG. 10.


With reference not to FIG. 12, a flowchart illustrating one embodiment of a process 1200 for dynamically controlling the natural frequency of the charging circuit 706 of the charging device 50 is shown. In some embodiments, the process 1200 can be performed using stored data identifying the natural frequency of the IPG 10, and specifically identifying the natural frequency of the charging circuit 607 of the IPG 10. In some embodiments, the use of stored IPG data can adequately characterize the natural frequency of the IPG 10 as the natural frequency of the IPG 10 can be constant after implantation. In some embodiments, controlling the natural frequency of the charging circuit 706 of the CD 50 with stored IPG data can improve system performance as compared to control of the natural frequency of the charging circuit 706 based on real-time data received from the IPG 10 as stored IPG data can be accessed regardless of the existence of a communicating connection between the IPG 10 and the CD 50 and as use of stored IPG data can allow quicker processing times and thus more responsive control of the natural frequency of the charging circuit 706 of the CD 50.


The process 1200 can be performed as part of, or in the place of the step depicted by block 1016 of FIG. 10. The process begins at block 1202 wherein the charging circuit 706 of the CD 50 is driven by, for example, the driver 708. In some embodiments, the driver 708 is configured to drive the charging circuit 706 of the CD 50 at the natural frequency of the charging circuit 706 of the CD 50.


After the charging circuit 706 is driven, or while the charging circuit is being driven, the process 1200 proceeds to block 1204 wherein it is determined whether to validate the natural frequency of the charging circuit 706 of the CD 50. In some embodiments, this validation can be periodically performed in which case the determination of whether to validate the natural frequency of the charging circuit 706 of the CD 50 can include determining whether the desired amount of time has passed since the last validation the natural frequency. In some embodiments, the natural frequency can be validated when a trigger signal is received from, for example, the IPG 10. In some embodiments, the trigger signal can be generated and sent by the IPG 10 when a change in charging efficiency is determined. If it is determined not to validate the natural frequency of the charging circuit 706, the process 1200 returns to block 1202 and proceeds as outlined above.


If it is determined to validate the natural frequency of the charging circuit 706, than the process 1200 proceeds to block 1206 wherein the natural frequency of the charging circuit 706 of the CD 50 is determined. In some embodiments, the natural frequency of the charging circuit 706 can be determined by determining the driving frequency of the driver 708. As previously mentioned, in some embodiments, the driver 708 can be configured to provide electrical pulses to the charging circuit 706 of the CD 50 at the natural frequency of the charging circuit 706. In such an embodiment, the frequency with which the driver 708 drives the charging circuit 706 can be retrieved from the driver 708 and the natural frequency of the charging circuit 706 can thereby be determined. Alternatively, in some embodiments, the natural frequency of the charging circuit 706 can be determined by termination of the driving of the charging circuit 706 by the driver 708 and the detection of frequency of ringing of the charging circuit 706. In some embodiments, this frequency of ringing corresponds to the natural frequency of the charging circuit.


After the natural frequency of the charging circuit 706 has been determined, the process 1200 proceeds to block 1208 wherein IPG data is retrieved. In some embodiments, the IPG data can include the characterization data for the IPG 10 which identifies the natural frequency of the IPG. In some embodiments, the IPG data can be retrieved from the data module 702, and specifically from one of the databases of the database module 702. In some embodiments, the IPG data can be generated by the characterization of the IPG 10 as discussed with reference to FIG. 11.


After the IPG data has been retrieved, the process 1200 proceeds to block 1210 wherein the natural frequency of the charging circuit 706 is determined and block 1206 is compared to the IPG data. In some embodiments this can include, for example, determining whether the natural frequency of the charging circuit 706 is greater than, less than, or equal to the natural frequency of the charging circuit 607 of the IPG 10 as identified in the IPG data. In some embodiments, a first value can be associated with the natural frequency of the charging circuit 706 when the natural frequency of the charging circuit 706 is greater than the natural frequency of the charging circuit 607 of the IPG 10, a second value can be associated with the natural frequency of the charging circuit 706 when the natural frequency of the charging circuit 706 is less than the natural frequency of the charging circuit 607 of the IPG 10, and the third value can be associated with the natural frequency of the charging circuit 706 when the natural frequency of the charging circuit 706 is equal to the natural frequency of the charging circuit 607 of the IPG 10.


After the natural frequency of the charging circuit 706 of the CD 50 is compared to the natural frequency of the charging circuit 607 of the IPG data as indicated in the IPG data, the process 1200 proceeds to decision state 1212 wherein it is determined if there is a mismatch between the natural frequency of the charging circuit 706 of the CD 50 and the natural frequency of the charging circuit 607 of the IPG 10. In some embodiments, this can be based on the results of the comparison of block 1210, and in some particular embodiments, this determination can include determining which of the first, second, and third values was associated with the natural frequency of the charging circuit 706 and block 1210. If it is determined that there is no mismatch between the natural frequency of the charging circuit 706 of the CD 50 and the IPG data, then the process 1200 returns to block 1202 and proceeds as outlined above.


If it is determined that there is a mismatch between the natural frequency of the charging circuit 706 of the CD 50 and the IPG data, then the process 1200 proceeds to block 1214 where the natural frequency correction is identified. In some embodiments, this can include identifying a change in the natural frequency of the charging circuit 706 that would result in a match between the natural frequency of the charging circuit 706 and the IPG data and/or minimize the difference between the natural frequency of charging circuit 706 and the IPG data. This determination can be based on the comparison performed in block 1210. In some embodiments, identifying the natural frequency correction in block 1214 can further include generating one or several control signals to change the natural frequency of the charging circuit 706 to thereby correct the natural frequency of the charging circuit 706.


After the natural frequency correction has been identified, the process 1200 proceeds to block 1216 wherein the charging circuit 706 of the CD 50 is reconfigured according to the natural frequency correction identified at block 1214. In some embodiments, this can include sending the control signals generated in block 1214 to the charging circuit 706 to thereby change the opened and closed state of one or several of the switches 810, 814, 818 and/or the inductance of the second inductor 900.


After the charging circuit has been reconfigured, the process 1200 can return to block 1202 and proceed as outlined above. In some embodiments, the process 1200 can be performed until charging of the IPG 10 is complete. In some embodiments, the completeness of the charging of the IPG 10 can be determined based on one or several signals received from the IPG 10 by the communication module 700 of the CD 50.


In the foregoing specification, the invention is described with reference to specific embodiments thereof, but those skilled in the art will recognize that the invention is not limited thereto. Various features and aspects of the above-described invention can be used individually or jointly. Further, the invention can be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive. It will be recognized that the terms “comprising,” “including,” and “having,” as used herein, are specifically intended to be read as open-ended terms of art.

Claims
  • 1. A neurostimulation system for delivering one or more electrical pulses to a target region within a patient's body, the neurostimulation system comprising: an implantable neurostimulator comprising: a hermetic housing having an external surface that is configured to be implanted within a body of a patient, the housing comprising a ceramic transmission region; anda charging circuit configured to receive power through the ceramic region of the hermetic housing; anda charger for coupling with an implantable neurostimulator, the charger comprising: a resonant circuit, wherein the resonant circuit is configurable to a plurality of natural frequencies; anda processor coupled to the resonant circuit to control the natural frequency of the resonant circuit according to stored data identifying a previously determined natural frequency of the charging circuit of the implantable neurostimulator.
  • 2. The neurostimulation system of claim 1, wherein the processor is configured to control the natural frequency of the resonant circuit according to characterization data identifying a natural frequency of the charging circuit of the implantable neurostimulator.
  • 3. The neurostimulation system of claim 2, wherein the characterization data is uniquely associated with the implantable neurostimulator, and wherein the characterization data is stored in a database in memory at the charger.
  • 4. The neurostimulation system of claim 3, wherein the characterization data is received at the charger from the implantable neurostimulator when the charger is coupled with the implantable neurostimulator.
  • 5. The neurostimulation system of claim 3, wherein the characterization data is generated by the charger when the database does not contain characterization data for the implantable neurostimulator.
  • 6. The neurostimulation system of claim 5, wherein generating the characterization data comprises: controlling the natural frequency of the resonant circuit to iteratively cycle through a plurality of natural frequencies of the resonant circuit;iteratively receiving data from the implantable neurostimulator indicative of a level of matching between the plurality of natural frequencies of the resonant circuit and a natural frequency of the charging circuit of the implantable neurostimulator; andidentifying one of the plurality of natural frequencies as the natural frequency of the charging circuit of the implantable neurostimulator.
  • 7. The neurostimulation system of claim 6, wherein the identified one of the plurality of natural frequencies is the one of the plurality of natural frequencies that best matches the natural frequency of the charging circuit of the implantable neurostimulator.
  • 8. The neurostimulation system of claim 7, wherein the identified one of the plurality of natural frequencies is the one of the plurality of natural frequencies that best matches the natural frequency of the charging circuit of the implantable neurostimulator over a statistically significant number of iterations.
  • 9. The neurostimulation system of claim 7, wherein the identified one of the plurality of natural frequencies is a default natural frequency.
  • 10. A method for coupling a charger with an implantable neurostimulator for delivering one or more electrical pulses to a target region within a patient's body, the method comprising: powering a resonant circuit of a charger with a driver coupled to a power source, the driver powering the resonant circuit at a driving frequency, wherein the resonant circuit is configurable to a plurality of natural frequencies;determining a first natural frequency of the resonant circuit;retrieving stored characterization data, wherein the characterization data identifies a previously determined natural frequency of a resonant circuit of the implantable neurostimulator; andchanging the natural frequency of the resonant circuit from the first natural frequency to a second natural frequency with control signals generated by a processor according to the characterization data.
  • 11. The method of claim 10, wherein determining the natural frequency of the resonant circuit of the charger comprises at least one of: determining the driving frequency of the driver; or detecting ringing of the resonant circuit.
  • 12. The method of claim 11, wherein the resonant circuit comprises: an inductor;a first capacitor coupled in series to the inductor; anda plurality of capacitors switchably coupleable to the inductor, wherein the plurality of capacitors are each configured to be in parallel with the first capacitor when switchably coupled to the inductor.
  • 13. The method of claim 12, wherein the plurality of capacitors comprises three capacitors.
  • 14. The method of claim 13, wherein each of the plurality of capacitors is switchably coupleable to the inductor via a transistor.
  • 15. The method of claim 14, wherein changing the natural frequency of the resonant circuit of the charger from the first natural frequency to a second natural frequency comprises: identifying a first switch configuration of the resonant circuit resulting in the first natural frequency;identifying a second switch configuration of the resonant circuit resulting in the second natural frequency; andgenerating a control signal to control at least one of: opening of at least one switch in the resonant circuit to disconnect at least one of the plurality of capacitors from the inductor; or closing of at least one switch in the resonant circuit to connect at least one of the plurality of capacitors from the inductor.
  • 16. The method of claim 11, wherein changing the natural frequency of the resonant circuit of the charger from the first natural frequency to a second natural frequency comprises: identifying a first inductance of an inductor in the resonant circuit resulting in the first natural frequency;identifying a second inductance of the inductor in the resonant circuit resulting in the second natural frequency; andgenerating a control signal to change the inductance of the inductor from the first inductance to the second inductance.
  • 17. The method of claim 10, wherein the characterization data is retrieved from a database in memory at the charger.
  • 18. The method of claim 10, wherein the characterization data is generated by the charger.
  • 19. The method of claim 18, wherein generating the characterization data comprises: controlling the natural frequency of the resonant circuit to iteratively cycle through a plurality of natural frequencies of the resonant circuit;iteratively receiving data from the implantable neurostimulator indicative of a level of matching between the plurality of natural frequencies of the resonant circuit and a natural frequency of the resonant circuit of the implantable neurostimulator; andidentifying one of the plurality of natural frequencies as the natural frequency of the resonant circuit of the implantable neurostimulator.
CROSS-REFERENCES TO RELATED APPLICATIONS

The present application is a divisional of U.S. Non-Provisional application Ser. No. 15/416,888 filed on Jan. 26, 2017, and entitled “Methods and Systems for Frequency Adjustment to Optimize Charging of Implantable Neurostimulator,” which claims the benefit of priority of U.S. Provisional Application No. 62/289,073 filed on Jan. 29, 2016, and entitled “Methods and Systems for Frequency Adjustment to Optimize Charging of Implantable Neurostimulator,” the contents of all of which are incorporated herein by reference in their entirety for all purposes.

US Referenced Citations (635)
Number Name Date Kind
3057356 Greatbach Nov 1962 A
3348548 Chardack Oct 1967 A
3646940 Timm et al. Mar 1972 A
3824129 Fagan, Jr. Jul 1974 A
3825015 Berkovits Jul 1974 A
3888260 Fischell Jun 1975 A
3902501 Citron et al. Sep 1975 A
3939843 Smyth Feb 1976 A
3942535 Schulman Mar 1976 A
3970912 Hoffman Jul 1976 A
3995623 Blake et al. Dec 1976 A
4019518 Maurer et al. Apr 1977 A
4044774 Corbin et al. Aug 1977 A
4082097 Mann et al. Apr 1978 A
4141365 Fischell et al. Feb 1979 A
4166469 Littleford Sep 1979 A
4269198 Stokes May 1981 A
4285347 Hess Aug 1981 A
4340062 Thompson et al. Jul 1982 A
4379462 Borkan et al. Apr 1983 A
4407303 Akerstrom Oct 1983 A
4437475 White Mar 1984 A
4468723 Hughes Aug 1984 A
4512351 Pohndorf Apr 1985 A
4550731 Batina et al. Nov 1985 A
4553702 Coffee et al. Nov 1985 A
4654880 Sontag Mar 1987 A
4662382 Sluetz et al. May 1987 A
4673867 Davis Jun 1987 A
4719919 Marchosky et al. Jan 1988 A
4721118 Harris Jan 1988 A
4722353 Sluetz Feb 1988 A
4744371 Harris May 1988 A
4800898 Hess et al. Jan 1989 A
4848352 Pohndorf et al. Jul 1989 A
4860446 Lessar et al. Aug 1989 A
4957118 Erlebacher Sep 1990 A
4989617 Memberg et al. Feb 1991 A
5012176 Laforge Apr 1991 A
5052407 Hauser et al. Oct 1991 A
5143089 Alt Sep 1992 A
5193539 Schulman et al. Mar 1993 A
5197466 Marchosky et al. Mar 1993 A
5204611 Nor et al. Apr 1993 A
5255691 Otten Oct 1993 A
5257634 Kroll Nov 1993 A
5342408 Decoriolis et al. Aug 1994 A
5439485 Mar et al. Aug 1995 A
5476499 Hirschberg Dec 1995 A
5484445 Knuth Jan 1996 A
5571148 Loeb et al. Nov 1996 A
5592070 Mino Jan 1997 A
5637981 Nagai et al. Jun 1997 A
5676162 Larson, Jr. et al. Oct 1997 A
5690693 Wang et al. Nov 1997 A
5702428 Tippey et al. Dec 1997 A
5702431 Wang et al. Dec 1997 A
5712795 Layman et al. Jan 1998 A
5713939 Nedungadi et al. Feb 1998 A
5733313 Barreras, Sr. et al. Mar 1998 A
5735887 Barreras, Sr. et al. Apr 1998 A
5741316 Chen et al. Apr 1998 A
5871532 Schroeppel Feb 1999 A
5876423 Braun Mar 1999 A
5877472 Campbell et al. Mar 1999 A
5902331 Bonner et al. May 1999 A
5948006 Mann Sep 1999 A
5949632 Barreras, Sr. et al. Sep 1999 A
5957965 Moumane et al. Sep 1999 A
5991665 Wang et al. Nov 1999 A
6027456 Feler et al. Feb 2000 A
6035237 Schulman et al. Mar 2000 A
6052624 Mann Apr 2000 A
6055456 Gerber Apr 2000 A
6057513 Ushikoshi et al. May 2000 A
6067474 Schulman et al. May 2000 A
6075339 Reipur et al. Jun 2000 A
6076017 Taylor et al. Jun 2000 A
6081097 Seri et al. Jun 2000 A
6083247 Rutten et al. Jul 2000 A
6104957 Alo et al. Aug 2000 A
6104960 Duysens et al. Aug 2000 A
6138681 Chen et al. Oct 2000 A
6164284 Schulman et al. Dec 2000 A
6165180 Cigaina et al. Dec 2000 A
6166518 Echarri et al. Dec 2000 A
6169387 Kaib Jan 2001 B1
6172556 Prentice Jan 2001 B1
6178353 Griffith et al. Jan 2001 B1
6181105 Cutolo et al. Jan 2001 B1
6185452 Schulman et al. Feb 2001 B1
6191365 Avellanet Feb 2001 B1
6208894 Schulman et al. Mar 2001 B1
6212430 Kung Apr 2001 B1
6212431 Hahn et al. Apr 2001 B1
6221513 Lasater Apr 2001 B1
6227204 Baumann et al. May 2001 B1
6243608 Pauly et al. Jun 2001 B1
6246911 Seligman Jun 2001 B1
6249703 Stanton et al. Jun 2001 B1
6265789 Honda et al. Jul 2001 B1
6275737 Mann Aug 2001 B1
6278258 Echarri et al. Aug 2001 B1
6305381 Weijand et al. Oct 2001 B1
6306100 Prass Oct 2001 B1
6313779 Leung et al. Nov 2001 B1
6315721 Schulman et al. Nov 2001 B2
6316909 Honda et al. Nov 2001 B1
6321118 Hahn Nov 2001 B1
6327504 Dolgin et al. Dec 2001 B1
6354991 Gross et al. Mar 2002 B1
6360750 Gerber et al. Mar 2002 B1
6381496 Meadows et al. Apr 2002 B1
6393325 Mann et al. May 2002 B1
6427086 Fischell et al. Jul 2002 B1
6438423 Rezai et al. Aug 2002 B1
6442434 Zarinetchi et al. Aug 2002 B1
6453198 Torgerson et al. Sep 2002 B1
6466817 Kaula et al. Oct 2002 B1
6473652 Sarwal et al. Oct 2002 B1
6500141 Irion Dec 2002 B1
6505075 Weiner Jan 2003 B1
6505077 Kast et al. Jan 2003 B1
6510347 Borkan Jan 2003 B2
6516227 Meadows et al. Feb 2003 B1
6517227 Stidham et al. Feb 2003 B2
6521350 Fey et al. Feb 2003 B2
6542846 Miller et al. Apr 2003 B1
6553263 Meadows et al. Apr 2003 B1
6564807 Schulman et al. May 2003 B1
6584355 Stessman Jun 2003 B2
6600954 Cohen et al. Jul 2003 B2
6609031 Law et al. Aug 2003 B1
6609032 Woods et al. Aug 2003 B1
6609945 Jimenez et al. Aug 2003 B2
6652449 Gross et al. Nov 2003 B1
6662051 Eraker et al. Dec 2003 B1
6664763 Echarri et al. Dec 2003 B2
6685638 Taylor et al. Feb 2004 B1
6721603 Zabara et al. Apr 2004 B2
6735474 Loeb et al. May 2004 B1
6745077 Griffith et al. Jun 2004 B1
6809701 Amundson et al. Oct 2004 B2
6836684 Rijkhoff et al. Dec 2004 B1
6847849 Mamo et al. Jan 2005 B2
6864755 Moore Mar 2005 B2
6892098 Ayal et al. May 2005 B2
6895280 Meadows et al. May 2005 B2
6896651 Gross et al. May 2005 B2
6901287 Davis et al. May 2005 B2
6941171 Mann et al. Sep 2005 B2
6971393 Mamo et al. Dec 2005 B1
6986453 Jiang et al. Jan 2006 B2
6989200 Byers et al. Jan 2006 B2
6990376 Tanagho et al. Jan 2006 B2
6999819 Swoyer et al. Feb 2006 B2
7051419 Schrom et al. May 2006 B2
7054689 Whitehurst et al. May 2006 B1
7069081 Biggs et al. Jun 2006 B2
7114502 Schulman et al. Oct 2006 B2
7127298 He et al. Oct 2006 B1
7131996 Wasserman et al. Nov 2006 B2
7142925 Bhadra et al. Nov 2006 B1
7146219 Sieracki et al. Dec 2006 B2
7151914 Brewer Dec 2006 B2
7167749 Biggs et al. Jan 2007 B2
7167756 Torgerson et al. Jan 2007 B1
7177690 Woods et al. Feb 2007 B2
7177698 Klosterman et al. Feb 2007 B2
7181286 Sieracki et al. Feb 2007 B2
7184836 Meadows et al. Feb 2007 B1
7187978 Malek et al. Mar 2007 B2
7191005 Stessman Mar 2007 B2
7212110 Martin et al. May 2007 B1
7225028 Della Santina et al. May 2007 B2
7225032 Schmeling et al. May 2007 B2
7231254 DiLorenzo Jun 2007 B2
7234853 Givoletti Jun 2007 B2
7239918 Strother et al. Jul 2007 B2
7245972 Davis Jul 2007 B2
7286880 Olson et al. Oct 2007 B2
7295878 Meadows et al. Nov 2007 B1
7305268 Gliner et al. Dec 2007 B2
7317948 King et al. Jan 2008 B1
7324852 Barolat et al. Jan 2008 B2
7324853 Ayal et al. Jan 2008 B2
7328068 Spinelli et al. Feb 2008 B2
7330764 Swoyer et al. Feb 2008 B2
7331499 Jiang et al. Feb 2008 B2
7359751 Erickson et al. Apr 2008 B1
7369894 Gerber May 2008 B2
7386348 North et al. Jun 2008 B2
7387603 Gross et al. Jun 2008 B2
7396265 Darley et al. Jul 2008 B2
7415308 Gerber et al. Aug 2008 B2
7444181 Shi et al. Oct 2008 B2
7444184 Boveja et al. Oct 2008 B2
7450991 Smith et al. Nov 2008 B2
7460911 Cosendai et al. Dec 2008 B2
7463928 Lee et al. Dec 2008 B2
7470236 Kelleher et al. Dec 2008 B1
7483752 Von Arx et al. Jan 2009 B2
7486048 Tsukamoto et al. Feb 2009 B2
7496404 Meadows et al. Feb 2009 B2
7513257 Schulman et al. Apr 2009 B2
7515967 Phillips et al. Apr 2009 B2
7532936 Erickson et al. May 2009 B2
7539538 Parramon et al. May 2009 B2
7551960 Forsberg et al. Jun 2009 B2
7555346 Woods et al. Jun 2009 B1
7565203 Greenberg et al. Jul 2009 B2
7578819 Bleich et al. Aug 2009 B2
7580752 Gerber et al. Aug 2009 B2
7582053 Gross et al. Sep 2009 B2
7617002 Goetz Nov 2009 B2
7636602 Baru Fassio et al. Dec 2009 B2
7640059 Forsberg et al. Dec 2009 B2
7643880 Tanagho et al. Jan 2010 B2
7650192 Wahlstrand Jan 2010 B2
7706889 Gerber et al. Apr 2010 B2
7720547 Denker et al. May 2010 B2
7725191 Greenberg et al. May 2010 B2
7734355 Cohen et al. Jun 2010 B2
7738963 Hickman et al. Jun 2010 B2
7738965 Phillips et al. Jun 2010 B2
7747330 Nolan et al. Jun 2010 B2
7771838 He et al. Aug 2010 B1
7774069 Olson et al. Aug 2010 B2
7801619 Gerber et al. Sep 2010 B2
7813803 Heruth et al. Oct 2010 B2
7813809 Strother et al. Oct 2010 B2
7826901 Lee et al. Nov 2010 B2
7848818 Barolat et al. Dec 2010 B2
7878207 Goetz et al. Feb 2011 B2
7904167 Klosterman et al. Mar 2011 B2
7912555 Swoyer et al. Mar 2011 B2
7925357 Phillips et al. Apr 2011 B2
7932696 Peterson Apr 2011 B2
7933656 Sieracki et al. Apr 2011 B2
7935051 Miles et al. May 2011 B2
7937158 Erickson et al. May 2011 B2
7952349 Huang et al. May 2011 B2
7957818 Swoyer Jun 2011 B2
7979119 Kothandaraman et al. Jul 2011 B2
7979126 Payne et al. Jul 2011 B2
7988507 Darley et al. Aug 2011 B2
8000782 Gharib et al. Aug 2011 B2
8000800 Takeda et al. Aug 2011 B2
8000805 Swoyer et al. Aug 2011 B2
8005535 Gharib et al. Aug 2011 B2
8005549 Boser et al. Aug 2011 B2
8005550 Boser et al. Aug 2011 B2
8019423 Possover Sep 2011 B2
8024047 Olson et al. Sep 2011 B2
8036756 Swoyer et al. Oct 2011 B2
8044635 Peterson Oct 2011 B2
8050769 Gharib et al. Nov 2011 B2
8055337 Moffitt et al. Nov 2011 B2
8068912 Kaula et al. Nov 2011 B2
8083663 Gross et al. Dec 2011 B2
8103360 Foster Jan 2012 B2
8116862 Stevenson et al. Feb 2012 B2
8121701 Woods et al. Feb 2012 B2
8129942 Park et al. Mar 2012 B2
8131358 Moffitt et al. Mar 2012 B2
8140168 Olson et al. Mar 2012 B2
8145324 Stevenson et al. Mar 2012 B1
8150530 Wesselink Apr 2012 B2
8175717 Haller et al. May 2012 B2
8180451 Hickman et al. May 2012 B2
8180452 Shaquer May 2012 B2
8180461 Mamo et al. May 2012 B2
8214042 Ozawa et al. Jul 2012 B2
8214048 Whitehurst et al. Jul 2012 B1
8214051 Sieracki et al. Jul 2012 B2
8219196 Torgerson Jul 2012 B2
8219202 Giftakis et al. Jul 2012 B2
8224460 Schleicher et al. Jul 2012 B2
8233990 Goetz Jul 2012 B2
8255057 Fang et al. Aug 2012 B2
8311636 Gerber et al. Nov 2012 B2
8314594 Scott et al. Nov 2012 B2
8332040 Winstrom Dec 2012 B1
8340786 Gross et al. Dec 2012 B2
8362742 Kallmyer Jan 2013 B2
8369943 Shuros et al. Feb 2013 B2
8386048 McClure et al. Feb 2013 B2
8417346 Giftakis et al. Apr 2013 B2
8423146 Giftakis et al. Apr 2013 B2
8447402 Jiang et al. May 2013 B1
8447408 North et al. May 2013 B2
8457756 Rahman Jun 2013 B2
8457758 Olson et al. Jun 2013 B2
8467875 Bennett et al. Jun 2013 B2
8480437 Dilmaghanian et al. Jul 2013 B2
8494625 Hargrove Jul 2013 B2
8515545 Trier Aug 2013 B2
8538530 Orinski Sep 2013 B1
8543223 Sage et al. Sep 2013 B2
8544322 Minami et al. Oct 2013 B2
8549015 Barolat Oct 2013 B2
8554322 Olson et al. Oct 2013 B2
8555894 Schulman et al. Oct 2013 B2
8562539 Marino Oct 2013 B2
8571677 Torgerson et al. Oct 2013 B2
8577474 Rahman et al. Nov 2013 B2
8588917 Whitehurst et al. Nov 2013 B2
8612002 Faltys et al. Dec 2013 B2
8620436 Parramon et al. Dec 2013 B2
8620454 Wahlstrand et al. Dec 2013 B2
8626314 Swoyer et al. Jan 2014 B2
8644933 Ozawa et al. Feb 2014 B2
8655451 Klosterman et al. Feb 2014 B2
8655453 Werder et al. Feb 2014 B2
8700175 Fell Apr 2014 B2
8706254 Vamos et al. Apr 2014 B2
8725262 Olson et al. May 2014 B2
8725269 Nolan et al. May 2014 B2
8738141 Smith et al. May 2014 B2
8738148 Olson et al. May 2014 B2
8750985 Parramon et al. Jun 2014 B2
8761897 Kaula et al. Jun 2014 B2
8768452 Gerber Jul 2014 B2
8774912 Gerber Jul 2014 B2
8774924 Weiner Jul 2014 B2
8774942 Lund et al. Jul 2014 B2
8805524 Woods et al. Aug 2014 B2
8855767 Faltys et al. Oct 2014 B2
8918174 Woods et al. Dec 2014 B2
8954148 Labbe et al. Feb 2015 B2
8989861 Su et al. Mar 2015 B2
9044592 Imran et al. Jun 2015 B2
9050473 Woods et al. Jun 2015 B2
9089712 Joshi et al. Jul 2015 B2
9108063 Olson et al. Aug 2015 B2
9144680 Kaula et al. Sep 2015 B2
9149635 Denison et al. Oct 2015 B2
9155885 Wei et al. Oct 2015 B2
9166321 Smith et al. Oct 2015 B2
9166441 Dearden et al. Oct 2015 B2
9168374 Su Oct 2015 B2
9192763 Gerber et al. Nov 2015 B2
9197173 Denison et al. Nov 2015 B2
9199075 Westlund Dec 2015 B1
9205255 Strother et al. Dec 2015 B2
9209634 Cottrill et al. Dec 2015 B2
9216294 Bennett et al. Dec 2015 B2
9227055 Wahlstrand et al. Jan 2016 B2
9227076 Sharma et al. Jan 2016 B2
9238135 Goetz et al. Jan 2016 B2
9240630 Joshi Jan 2016 B2
9242090 Atalar et al. Jan 2016 B2
9244898 Vamos et al. Jan 2016 B2
9248292 Trier et al. Feb 2016 B2
9259578 Torgerson et al. Feb 2016 B2
9259582 Joshi et al. Feb 2016 B2
9265958 Joshi et al. Feb 2016 B2
9270134 Gaddam et al. Feb 2016 B2
9272140 Gerber et al. Mar 2016 B2
9283394 Whitehurst et al. Mar 2016 B2
9295851 Gordon et al. Mar 2016 B2
9308022 Chitre et al. Apr 2016 B2
9308382 Strother et al. Apr 2016 B2
9314616 Wells et al. Apr 2016 B2
9320899 Parramon et al. Apr 2016 B2
9333339 Weiner May 2016 B2
9352148 Stevenson et al. May 2016 B2
9352150 Stevenson et al. May 2016 B2
9358039 Kimmel et al. Jun 2016 B2
9364658 Wechter Jun 2016 B2
9375574 Kaula et al. Jun 2016 B2
9393423 Parramon et al. Jul 2016 B2
9399137 Parker et al. Jul 2016 B2
9409020 Parker Aug 2016 B2
9415211 Bradley et al. Aug 2016 B2
9427571 Sage et al. Aug 2016 B2
9427573 Gindele et al. Aug 2016 B2
9433783 Wei et al. Sep 2016 B2
9436481 Drew Sep 2016 B2
9446245 Grill et al. Sep 2016 B2
9463324 Olson et al. Oct 2016 B2
9468755 Westlund et al. Oct 2016 B2
9471753 Kaula et al. Oct 2016 B2
9480846 Strother et al. Nov 2016 B2
9492672 Vamos et al. Nov 2016 B2
9492675 Torgerson et al. Nov 2016 B2
9492678 Chow Nov 2016 B2
9498628 Kaemmerer et al. Nov 2016 B2
9502754 Zhao et al. Nov 2016 B2
9504830 Kaula et al. Nov 2016 B2
9522282 Chow et al. Dec 2016 B2
9592389 Moffitt Mar 2017 B2
9610449 Kaula et al. Apr 2017 B2
9615744 Denison et al. Apr 2017 B2
9623257 Olson et al. Apr 2017 B2
9636497 Bradley et al. May 2017 B2
9643004 Gerber May 2017 B2
9653935 Cong et al. May 2017 B2
9656074 Simon et al. May 2017 B2
9656076 Trier et al. May 2017 B2
9656089 Yip et al. May 2017 B2
9675809 Chow Jun 2017 B2
9687649 Thacker Jun 2017 B2
9707405 Shishilla et al. Jul 2017 B2
9713706 Gerber Jul 2017 B2
9717900 Swoyer et al. Aug 2017 B2
9724526 Strother et al. Aug 2017 B2
9731116 Chen Aug 2017 B2
9737704 Wahlstrand et al. Aug 2017 B2
9744347 Chen et al. Aug 2017 B2
9750930 Chen Sep 2017 B2
9757555 Novotny et al. Sep 2017 B2
9764147 Torgerson Sep 2017 B2
9767255 Kaula et al. Sep 2017 B2
9776002 Parker et al. Oct 2017 B2
9776006 Parker et al. Oct 2017 B2
9776007 Kaula et al. Oct 2017 B2
9782596 Vamos et al. Oct 2017 B2
9814884 Parker et al. Nov 2017 B2
9821112 Olson et al. Nov 2017 B2
9827415 Stevenson et al. Nov 2017 B2
9827424 Kaula et al. Nov 2017 B2
9833614 Gliner Dec 2017 B1
9849278 Spinelli et al. Dec 2017 B2
9855438 Parramon et al. Jan 2018 B2
9872988 Kaula et al. Jan 2018 B2
9878165 Wilder et al. Jan 2018 B2
9878168 Shishilla et al. Jan 2018 B2
9882420 Cong et al. Jan 2018 B2
9884198 Parker Feb 2018 B2
9889292 Gindele et al. Feb 2018 B2
9889293 Siegel et al. Feb 2018 B2
9889306 Stevenson et al. Feb 2018 B2
9895532 Kaula et al. Feb 2018 B2
9899778 Hanson et al. Feb 2018 B2
9901284 Olsen et al. Feb 2018 B2
9901740 Drees et al. Feb 2018 B2
9907476 Bonde et al. Mar 2018 B2
9907955 Bakker et al. Mar 2018 B2
9907957 Woods et al. Mar 2018 B2
9924904 Cong et al. Mar 2018 B2
9931513 Kelsch et al. Apr 2018 B2
9931514 Frysz et al. Apr 2018 B2
9950171 Johanek et al. Apr 2018 B2
9974108 Polefko May 2018 B2
9974949 Thompson et al. May 2018 B2
9981121 Seifert et al. May 2018 B2
9981137 Eiger May 2018 B2
9987493 Torgerson et al. Jun 2018 B2
9993650 Seitz et al. Jun 2018 B2
9999765 Stevenson Jun 2018 B2
10004910 Gadagkar et al. Jun 2018 B2
10016596 Stevenson et al. Jul 2018 B2
10027157 Labbe et al. Jul 2018 B2
10045764 Scott et al. Aug 2018 B2
10046164 Gerber Aug 2018 B2
10047782 Sage et al. Aug 2018 B2
10052490 Kaula et al. Aug 2018 B2
10065044 Sharma et al. Sep 2018 B2
10071247 Childs Sep 2018 B2
10076661 Wei et al. Sep 2018 B2
10076667 Kaula et al. Sep 2018 B2
10083261 Kaula et al. Sep 2018 B2
10086191 Bonde et al. Oct 2018 B2
10086203 Kaemmerer Oct 2018 B2
10092747 Sharma et al. Oct 2018 B2
10092749 Stevenson et al. Oct 2018 B2
10095837 Corey et al. Oct 2018 B2
10099051 Stevenson et al. Oct 2018 B2
10103559 Cottrill et al. Oct 2018 B2
10109844 Dai et al. Oct 2018 B2
10118037 Kaula et al. Nov 2018 B2
10124164 Stevenson et al. Nov 2018 B2
10124171 Kaula et al. Nov 2018 B2
10124179 Norton et al. Nov 2018 B2
10141545 Kraft et al. Nov 2018 B2
10173062 Parker Jan 2019 B2
10179241 Walker et al. Jan 2019 B2
10179244 Lebaron et al. Jan 2019 B2
10183162 Johnson et al. Jan 2019 B2
10188857 North et al. Jan 2019 B2
10195419 Shiroff et al. Feb 2019 B2
10206710 Kern et al. Feb 2019 B2
10213229 Chitre et al. Feb 2019 B2
10220210 Walker et al. Mar 2019 B2
10226617 Finley et al. Mar 2019 B2
10226636 Gaddam et al. Mar 2019 B2
10236709 Decker et al. Mar 2019 B2
10238863 Gross et al. Mar 2019 B2
10238877 Kaula et al. Mar 2019 B2
10244956 Kane Apr 2019 B2
10245434 Kaula et al. Apr 2019 B2
10258800 Perryman et al. Apr 2019 B2
10265532 Carcieri et al. Apr 2019 B2
10277055 Peterson et al. Apr 2019 B2
10293168 Bennett et al. May 2019 B2
10328253 Wells Jun 2019 B2
10363419 Simon et al. Jul 2019 B2
10369275 Olson et al. Aug 2019 B2
10369370 Shishilla et al. Aug 2019 B2
10376701 Kaula et al. Aug 2019 B2
10448889 Gerber et al. Oct 2019 B2
10456574 Chen et al. Oct 2019 B2
10471262 Perryman et al. Nov 2019 B2
10485970 Gerber et al. Nov 2019 B2
10493282 Caparso et al. Dec 2019 B2
10493287 Yoder et al. Dec 2019 B2
10561835 Gerber Feb 2020 B2
10603500 Nassif et al. Mar 2020 B2
20020040185 Atalar et al. Apr 2002 A1
20020051550 Leysieffer May 2002 A1
20020051551 Leysieffer et al. May 2002 A1
20020116042 Boling Aug 2002 A1
20020140399 Echarri et al. Oct 2002 A1
20020177884 Ahn et al. Nov 2002 A1
20030028072 Fischell et al. Feb 2003 A1
20030040291 Brewer Feb 2003 A1
20030078633 Firlik et al. Apr 2003 A1
20030114899 Woods et al. Jun 2003 A1
20030212440 Boveja Nov 2003 A1
20040098068 Carbunaru et al. May 2004 A1
20040106963 Tsukamoto et al. Jun 2004 A1
20040210271 Campen Oct 2004 A1
20040210272 Erickson Oct 2004 A1
20040210290 Omar-Pasha Oct 2004 A1
20040250820 Forsell Dec 2004 A1
20040267137 Peszynski et al. Dec 2004 A1
20050004619 Wahlstrand et al. Jan 2005 A1
20050004621 Boveja et al. Jan 2005 A1
20050021108 Klosterman et al. Jan 2005 A1
20050075693 Toy et al. Apr 2005 A1
20050075694 Schmeling et al. Apr 2005 A1
20050075696 Forsberg et al. Apr 2005 A1
20050075697 Olson et al. Apr 2005 A1
20050075698 Phillips et al. Apr 2005 A1
20050075699 Olson et al. Apr 2005 A1
20050075700 Schommer et al. Apr 2005 A1
20050104577 Matei et al. May 2005 A1
20050187590 Boveja et al. Aug 2005 A1
20060016452 Goetz et al. Jan 2006 A1
20060050539 Yang et al. Mar 2006 A1
20060142822 Tulgar Jun 2006 A1
20060149345 Boggs et al. Jul 2006 A1
20060200205 Haller Sep 2006 A1
20060206166 Weiner Sep 2006 A1
20070032836 Thrope et al. Feb 2007 A1
20070073357 Rooney et al. Mar 2007 A1
20070239224 Bennett et al. Oct 2007 A1
20070265675 Lund et al. Nov 2007 A1
20070293914 Woods et al. Dec 2007 A1
20080065182 Strother et al. Mar 2008 A1
20080081962 Miller et al. Apr 2008 A1
20080132961 Jaax et al. Jun 2008 A1
20080132969 Bennett et al. Jun 2008 A1
20080154335 Thrope et al. Jun 2008 A1
20080161874 Bennett et al. Jul 2008 A1
20080172109 Rahman et al. Jul 2008 A1
20080183236 Gerber Jul 2008 A1
20080278974 Wu Nov 2008 A1
20090088816 Harel et al. Apr 2009 A1
20090105785 Wei et al. Apr 2009 A1
20090112291 Wahlstrand et al. Apr 2009 A1
20090259273 Figueiredo et al. Oct 2009 A1
20090284220 Toncich et al. Nov 2009 A1
20100022861 Cinbis Jan 2010 A1
20100076254 Jimenez et al. Mar 2010 A1
20100076534 Mock Mar 2010 A1
20100100158 Thrope et al. Apr 2010 A1
20100331918 Digiore et al. Dec 2010 A1
20110101790 Budgett May 2011 A1
20110152959 Sherwood et al. Jun 2011 A1
20110160810 Griffith Jun 2011 A1
20110257701 Strother et al. Oct 2011 A1
20110270269 Swoyer et al. Nov 2011 A1
20110278948 Forsell Nov 2011 A1
20110282416 Hamann et al. Nov 2011 A1
20110288615 Armstrong Nov 2011 A1
20110301667 Olson et al. Dec 2011 A1
20110313427 Gindele et al. Dec 2011 A1
20120016447 Zhu et al. Jan 2012 A1
20120041512 Weiner Feb 2012 A1
20120046712 Woods et al. Feb 2012 A1
20120071950 Archer Mar 2012 A1
20120119698 Karalis et al. May 2012 A1
20120130448 Woods et al. May 2012 A1
20120139485 Olson et al. Jun 2012 A1
20120235634 Hall et al. Sep 2012 A1
20120259381 Smith et al. Oct 2012 A1
20120262108 Olson et al. Oct 2012 A1
20120267960 Low et al. Oct 2012 A1
20120274270 Dinsmoor et al. Nov 2012 A1
20130004925 Labbe Jan 2013 A1
20130006330 Wilder et al. Jan 2013 A1
20130006331 Weisgarber et al. Jan 2013 A1
20130023958 Fell Jan 2013 A1
20130096651 Ozawa et al. Apr 2013 A1
20130148768 Kim Jun 2013 A1
20130150925 Vamos et al. Jun 2013 A1
20130197608 Eiger Aug 2013 A1
20130211479 Olson et al. Aug 2013 A1
20130303942 Damaser et al. Nov 2013 A1
20130310630 Smith et al. Nov 2013 A1
20130310894 Trier Nov 2013 A1
20130331909 Gerber Dec 2013 A1
20130331910 Lamont et al. Dec 2013 A1
20140031903 Mashiach et al. Jan 2014 A1
20140107743 Wahlstrand et al. Apr 2014 A1
20140207220 Boling et al. Jul 2014 A1
20140222112 Fell Aug 2014 A1
20140237806 Smith et al. Aug 2014 A1
20140277268 Lee Sep 2014 A1
20140277270 Parramon et al. Sep 2014 A1
20150005846 Ranu Jan 2015 A1
20150028806 Dearden et al. Jan 2015 A1
20150088227 Shishilla et al. Mar 2015 A1
20150094790 Shishilla et al. Apr 2015 A1
20150100106 Shishilla et al. Apr 2015 A1
20150134027 Kaula et al. May 2015 A1
20150214604 Zhao et al. Jul 2015 A1
20150290465 Mashiach Oct 2015 A1
20150335888 Demers Nov 2015 A1
20150335893 Parker Nov 2015 A1
20160022996 Kaula et al. Jan 2016 A1
20160114177 Colvin et al. Apr 2016 A1
20160126771 Aghassian et al. May 2016 A1
20160199658 Nassif Jul 2016 A1
20170189698 Dellamano et al. Jul 2017 A1
20170197079 Illegems et al. Jul 2017 A1
20170216609 Nassif et al. Aug 2017 A1
20170340878 Wahlstrand et al. Nov 2017 A1
20180021587 Strother et al. Jan 2018 A1
20180036477 Olson et al. Feb 2018 A1
20190269918 Parker Sep 2019 A1
20190351244 Shishilla et al. Nov 2019 A1
20190358395 Olson et al. Nov 2019 A1
Foreign Referenced Citations (46)
Number Date Country
520440 Sep 2011 AT
4664800 Nov 2000 AU
5123800 Nov 2000 AU
2371378 Nov 2000 CA
2554676 Sep 2005 CA
3146182 Jun 1983 DE
102010006837 Aug 2011 DE
0656218 Jun 1995 EP
1205004 May 2002 EP
1680182 Jul 2006 EP
1904153 Apr 2008 EP
2243509 Oct 2010 EP
2498873 Sep 2012 EP
2731673 May 2014 EP
2395128 Feb 2013 ES
1098715 Mar 2012 HK
2002198743 Jul 2002 JP
2003047179 Feb 2003 JP
2007268293 Oct 2007 JP
4125357 Jul 2008 JP
2011510787 Apr 2011 JP
2013123484 Jun 2013 JP
2013226041 Oct 2013 JP
9820933 May 1998 WO
9918879 Apr 1999 WO
9934870 Jul 1999 WO
9942173 Aug 1999 WO
0056677 Sep 2000 WO
0065682 Nov 2000 WO
0066221 Nov 2000 WO
0069012 Nov 2000 WO
0183029 Nov 2001 WO
2002003408 Jan 2002 WO
0209808 Feb 2002 WO
2004021876 Mar 2004 WO
2004103465 Dec 2004 WO
2005079295 Sep 2005 WO
2005081740 Sep 2005 WO
2008021524 Feb 2008 WO
2009051539 Apr 2009 WO
2009091267 Jul 2009 WO
2010042056 Apr 2010 WO
2010042057 Apr 2010 WO
2011059565 May 2011 WO
2013141884 Sep 2013 WO
2017132374 Aug 2017 WO
Non-Patent Literature Citations (51)
Entry
US 9,601,939 B2, 03/2017, Cong et al. (withdrawn)
“Bu-802a: How Does Rising Internal Resistance Affect Performance? Understanding the Importance of Low Conductivity”, BatteryUniversity.com, Available Online at https://batteryuniversity.com/learn/article/rising_internal_resistance, Accessed from Internet on: May 15, 2020, 10 pages.
“DOE Handbook: Primer on Lead-Acid Storage Batteries”, U.S. Dept. of Energy, Available Online at: htt12s://www.stan dards.doe.gov/standards- documents/ I 000/1084-bhdbk-1995/@@images/file, Sep. 1995, 54 pages.
“Medical Electrical Equipment—Part 1: General Requirements for Safety”, British Standard, BS EN 60601-1:1990-BS5724-1:1989, Mar. 1979, 200 pages.
“Summary of Safety and Effectiveness”, Medtronic InterStim System for Urinary Control, Apr. 15, 1999, pp. 1-18.
“The Advanced Bionics PRECISION™ Spinal Cord Stimulator System”, Advanced Bionics Corporation, Apr. 27, 2004, pp. 1-18.
“UL Standard for Safety for Medical and Dental Equipment”, UL 544, 4th edition, Dec. 30, 1998, 128 pages.
Barnhart et al., “A Fixed-Rate Rechargeable Cardiac Pacemaker”, APL Technical Digest, Jan.-Feb. 1970, pp. 2-9.
Benditt et al., “A Combined Atrial/Ventricular Lead for Permanent Dual-Chamber Cardiac Pacing Applications”, Chest, vol. 83, No. 6, Jun. 1983, pp. 929-931.
Boiocchi et al., “Self-Calibration in High Speed Current Steering CMOS D/A Converters”, Advanced A-D and D-A Conversion Techniques and their Applications, Second International Conference on Cambridge, Jul. 1994, pp. 148-152.
Bosch et al., “Sacral (S3) Segmental Nerve Stimulation as a Treatment for Urge Incontinence in Patients with Detrusor Instability: Results of Chronic Electrical Stimulation Using an Implantable Neural Prosthesis”, The Journal of Urology, vol. 154, No. 2, Aug. 1995, pp. 504-507.
Boyce et al., “Research Related to the Development of an Artificial Electrical Stimulator for the Paralyzed Human Bladder: a Review”, The Journal of Urology, vol. 91, No. 1, Jan. 1964, pp. 41-51.
Bradley et al., “Further Experience With the Radio Transmitter Receiver Unit for the Neurogenic Bladder”, Journal of Neurosurgery, vol. 20, No. 11, Nov. 1963, pp. 953-960.
Broggi et al., “Electrical Stimulation of the Gasserian Ganglion for Facial Pain: Preliminary Results”, Acta Neurochirurgica, vol. 39, 1987, pp. 144-146.
Cameron et al., “Effects of Posture on Stimulation Parameters in Spinal Cord Stimulation”, Neuromodulation, vol. 1, No. 4, Oct. 1998, pp. 177-183.
Connelly et al., “Atrial Pacing Leads Following Open Heart Surgery: Active or Passive Fixation?”, Pacing and Clinical Electrophysiology, vol. 20, No. 10, Oct. 1997, pp. 2429-2433.
Fischell , “The Development of Implantable Medical Devices at the Applied Physics Laboratory”, Johns Hopkins APL Technical Digest, vol. 13 No. 1, 1992, pp. 233-243.
Gaunt et al., “Control of Urinary Bladder Function With Devices: Successes and Failures”, Progress in Brain Research, vol. 152, 2006, pp. 1-24.
Ghovanloo et al., “A Small Size Large Voltage Compliance Programmable Current Source for Biomedical Implantable Microstimulators”, Proceedings of the 25th Annual International Conference of the IEEE EMBS, Sep. 17-21, 2003, pp. 1979-1982.
Gudnason , “A low-power ASK demodulator for inductively coupled implantable electronics”, Solid-State Circuits Conference, 2000, Esscirc 00, Proceedings of the 26RD European, IEEE, Sep. 19, 2000, pp. 385-388.
Helland , “Technical Improvements to be Achieved by the Year 2000: Leads and Connector Technology”, Rate Adaptive Cardiac Pacing, Springer Verlag, 1993, pp. 279-292.
Hidefjall “The Pace of Innovation—Patterns of Innovation in the Cardiac Pacemaker Industry”, Linkoping University Press, 1997, 398 pages.
Humayun, M.S., et al., “A Variable Range Bi-Phasic Current Stimulus Driver Circuitry for an Implantable Retinal Prosthetic Device”, IEEE Journal of Solid-State Circuits, IEEE Service Center, Piscataway, NJ, USA, vol. 40, No. 3, Mar. 1, 2005, (Mar. 1, 2005), pp. 763-771.
Ishihara et al., “A Comparative Study of Endocardial Pacemaker Leads”, Cardiovascular Surgery, Nagoya Ekisaikai Hospital, 1st Dept. of Surgery, Nagoya University School of Medicine, 1981, pp. 132-135.
Jonas et al., “Studies on the Feasibility of Urinary Bladder Evacuation by Direct Spinal Cord Stimulation. I. Parameters of Most Effective Stimulation”, Investigative urology, vol. 13, No. 2, 1975, pp. 142-150.
Kakuta et al., “In Vivo Long Term Evaluation of Transcutaneous Energy Transmission for Totally Implantable Artificial Heart”, ASAIO Journal, Mar.-Apr. 2000, pp. 1-2.
Kester et al., “Voltage-to-Frequency Converters”, Available Online at: https://www.analog.com/media/cn/training-seminars/tutorials/MT-028.pdf, 7 pages.
Lazorthes et al., “Chronic Stimulation of the Gasserian Ganglion for Treatment of Atypical Facial Neuralgia”, Pacing and Clinical Electrophysiology, vol. 10, Jan.-Feb. 1987, pp. 257-265.
Lewis et al., “Early Clinical Experience with the Rechargeable Cardiac Pacemaker”, The Annals of Thoracic Surgery, vol. 18, No. 5, Nov. 1974, pp. 490-493.
Love et al., “Experimental Testing of a Permanent Rechargeable Cardiac Pacemaker”, The Annals of Thoracic Surgery, vol. 17, No. 2, Feb. 1, 1974, pp. 152-156.
Love , “Pacemaker Troubleshooting and Follow-up”, Clinical Cardiac Pacing, Defibrillation, and Resynchronization Therapy, Chapter 24, 2007, pp. 1005-1062.
Madigan et al., “Difficulty of Extraction of Chronically Implanted Tined Ventricular Endocardial Leads”, Journal of the American College of Cardiology, vol. 3, No. 3, Mar. 1984, pp. 724-731.
Meglio , “Percutaneously Implantable Chronic Electrode for Radiofrequency Stimulation of the Gasserian Ganglion. A Perspective in the Management of Trigeminal Pain”, Acta Neurochirurgica, vol. 33, 1984, pp. 521-525.
Meyerson , “Alleviation of Atypical Trigeminal Pain by Stimulation of the Gasserian Ganglion via an Implanted Electrode”, Acta Neurochirurgica Supplementum , vol. 30, 1980, pp. 303-309.
Mitamura et al., “Development of Transcutaneous Energy Transmission System”, Available Online at https://www.researchgate.net/publication/312810915 Ch.28, Jan. 1988, pp. 265-270.
Nakamura et al., “Biocompatibility and Practicality Evaluations of Transcutaneous Energy Transmission Unit for the Totally Implantable Artifical Heart System”, Journal of Artificial Organs, vol. 27, No. 2, 1998, pp. 347-351.
Nashold et al., “Electromicturition in Paraplegia. Implantation of a Spinal Neuroprosthesis”, Arch Surg., vol. 104, Feb. 1972, pp. 195-202.
Painter et al., “Implantation of an Endocardial Tined Lead to Prevent Early Dislodgement”, The Journal of Thoracic and Cardiovascular Surgery, vol. 77, No. 2, Feb. 1979, pp. 249-251.
Paralikar et al., “A Fully Implantable and Rechargeable Neurostimulation System for Animal Research”, 7th Annual International IEEE EMBS Conference of Neural Engineering, Apr. 22-24, 2015, pp. 418-421.
Perez , “Lead-Acid Battery State of Charge vs. Voltage”, Available Online at http://www.rencobattery.com/resources/SOC vs-Voltage.pdf, Aug.-Sep. 1993, 5 pages.
Schaldach et al., “A Long-Lived, Reliable, Rechargeable Cardiac Pacemaker”, Engineering in Medicine, vol. 1: Advances in Pacemaker Technology, 1975, 34 pages.
Scheuer-Leeser et al., “Polyurethane Leads: Facts and Controversy”, PACE, vol. 6, Mar.-Apr. 1983, pp. 454-458.
Sivaprakasam et al., “A Variable Range Bi-Phasic Current Stimulus Driver Circuitry for an Implantable Retinal Prosthetic Device”, IEEE Journal of Solid-State Circuits, IEEE Service Center, Piscataway, NJ, USA, vol. 40, No. 3, Mar. 1, 2005, pp. 763-771.
Smith , “Changing Standards for Medical Equipment”, UL 544 and UL 187 vs. UL 2601 (“Smith”), 2002, 8 pages.
Tanagho et al., “Bladder Pacemaker: Scientific Basis and Clinical Future”, Urology, vol. 20, No. 6, Dec. 1982, pp. 614-619.
Tanagho , “Neuromodulation and Neurostimulation: Overview and Future Potential”, Translational Androl Urol, vol. 1, No. 1, 2012, pp. 44-49.
Torres et al., “Electrostatic Energy-Harvesting and Battery-Charging CMOS System Prototype”, Available Online at http://rincon mora.gatech.edu/12ublicat/jrnls/tcasi09_hrv_sys.pdf, pp. 1-10.
Van Paemel , “High-Efficiency Transmission for Medical Implants”, IEEE Solid-State Circuits Magazine, vol. 3, No. 1, Jan. 1, 2011, pp. 47-59.
Wang et al., “A 140-dB CMRR Low-Noise Instrumentation Amplifier for Neural Signal Sensing”, Circuits and Systems, 2006. APCCAS 2006, IEEE Asia Pacific Conference on IEEE, Piscataway, NZ, USA, Dec. 1, 2006, pp. 696-699.
Young , “Electrical Stimulation of the Trigeminal Nerve Root for the Treatment of Chronic Facial Pain”, Journal of Neurosurgery, vol. 83, No. 1, Jul. 1995, pp. 72-78.
U.S. Appl. No. 14/446,294, filed Jul. 29, 2014, 47 pages.
Related Publications (1)
Number Date Country
20200230427 A1 Jul 2020 US
Provisional Applications (1)
Number Date Country
62289073 Jan 2016 US
Divisions (1)
Number Date Country
Parent 15416888 Jan 2017 US
Child 16752539 US