System for determining an estimated battery capacity for an implantable device

Abstract

A system for determining an estimated value of the battery capacity of a battery of an implantable device used over a period of time. This system includes an external device having a processor configured to communicate with the implantable device. During an initial stage, the processor is configured to determine an estimated value of the battery capacity using battery energy consumption data and the battery capacity at the start of use. During a later stage, the processor is configured to determine an estimated value of the battery capacity based on the battery voltage. This system may include an intermediate stage, during which the processor is configured to determine an estimated value of the battery capacity based on the battery energy consumption data and the battery voltage.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application claims the priority and benefit of a U.S. Provisional Application filed on July 8, 2022, with application number 63 / 359,463, which is hereby incorporated by reference in its entirety into this specification.

[0002] (Technical Field) The present disclosure generally relates to implantable medical devices such as neural stimulation therapy systems, and more particularly, to methods and systems for determining an estimated remaining battery level for an implantable medical device. The remaining level is the amount of energy in the battery available at a given time.

Background Art

[0003] In recent years, treatment with implantable medical devices such as neural stimulation systems has become increasingly common. For example, stimulation systems often use an array of multiple electrodes to treat one or more target nerve structures. The multiple electrodes are often attached together to a multi - electrode lead, and the lead is implanted in the patient's tissue at a position intended to provide an electrical connection of the electrodes to the target nerve structure, and usually at least a portion of the connection is provided through intervening tissue. Other approaches can also be employed, such as attaching one or more electrodes to the skin covering the target nerve structure or implanting them in a cuff around the target nerve. In any case, a physician typically attempts to establish an appropriate treatment protocol by varying the electrical stimulation applied to the electrodes.

Summary of the Invention

Problems to be Solved by the Invention

[0004] The neural tissue structures of different patients can vary greatly. The electrical properties of the tissue structures surrounding the target neural structure can also vary significantly from patient to patient, the neural responses to stimulation can be markedly different, and an electrical stimulation pulse pattern, pulse width, frequency, and / or amplitude that is effective in affecting the body function of one patient may cause significant discomfort or pain, or have only a limited effect, in another patient. Even in patients for whom implantation of a nerve stimulation system provides effective treatment, frequent adjustment and modification of the stimulation protocol are often required before an appropriate treatment program can be determined, and patients often have to make repeated hospital visits or experience significant discomfort before effectiveness is achieved. Such implantable nerve stimulation devices often include a battery that meets the power requirements of the device when performing stimulation, in addition to control and telemetry functions.

[0005] Therefore, the lifespan of such devices and the battery life are limited and can vary significantly depending on the usage method. Previously, implantable systems with non-rechargeable batteries were typically replaced every 5 to 7 years. More recently, batteries that need to be replaced every 10 years or more have been used in implantable systems. Since replacement involves additional surgery, patient discomfort, and significant costs to the medical system, it is important for both patients and clinicians to accurately estimate the battery capacity regularly over the service life of the battery. Currently, there is often uncertainty about the remaining battery life, especially since battery parameters generally remain relatively constant over most of the battery's life and can deteriorate rapidly towards the end of its life. Current systems and methods for determining battery capacity often lack consistency and accurate assessment of the remaining battery life. Therefore, it is desirable to provide a more accurate and reliable prediction value, and / or estimate, of the remaining amount of the battery included in an implantable device, particularly for nerve stimulation systems, thereby significantly improving patient comfort and eliminating uncertainties regarding the planning and timing of device replacement.

Means for Solving the Problem

[0006] Aspects of the present disclosure relate to methods, systems, and devices for determining the battery charge level of a battery of an implantable device, such as an implantable pulse generator (IPG). Typically, the battery is a non-rechargeable primary battery, such as a lithium manganese dioxide (Li-MnO2) battery.

[0007] One aspect of the present disclosure relates to a method for determining an estimated battery charge level of a battery within an implantable device over the useful life of the device. Such a method may include establishing communication between the implantable device and an external device (e.g., a clinical medical programmer, a patient remote control, or another device), and receiving information including battery energy consumption data (e.g., a dataset of energy consumed by a specific load on the battery, or a cumulative value of energy consumed by a load on the battery) and the voltage of the battery from the implantable device. Based on the received information, the estimated battery charge level is determined. In some embodiments, a method that uses one or more techniques or equations to determine the estimated battery charge level may be used. In some embodiments, this technique or equation depends on stages of the useful life of the battery (e.g., an initial stage, a middle stage, a third stage, a later stage).

[0008] In some embodiments, during the initial stage of use, the estimated battery charge level is determined from data related to the cumulative battery energy consumption relative to the total capacity of the battery at full charge. During the middle stage of use, the estimated battery charge level is based on a combination of battery energy consumption data and battery voltage at the time when the estimated battery capacity is determined. During the third stage of use, the determination of the estimated battery charge level is voltage-based. In some embodiments, the method includes estimating the battery charge level from the cumulative battery energy consumption during the initial stage, and estimating the battery capacity based on voltage during the later stage. In some embodiments, the later stage is immediately after the initial stage, while in other embodiments, the later stage is the third stage or the final stage.

[0009] In some embodiments, during an intermediate stage, a first estimation result based on battery energy consumption data and a second estimation result based on voltage are linearly combined. In some embodiments, the intermediate stage is when the voltage is within a voltage range between an upper battery voltage threshold (e.g., 95% - 99% of the nominal open circuit voltage) and a lower battery voltage threshold (e.g., 85% - 95% of the nominal voltage). The nominal voltage can be provided by the battery manufacturer (e.g., 3.1V in the case of the Litronik LiS battery described later), and typically corresponds to the open circuit voltage of a new and unused battery. In some embodiments, the battery energy consumption data set and the voltage can be linearly combined such that a first estimation result based on the battery energy consumption data is fully weighted at the start of the intermediate stage, and a second estimation result based on the voltage is fully weighted at the end of the intermediate stage.

[0010] In some embodiments, a third stage occurs when the voltage is less than the lower voltage threshold (e.g., between 85% - 95% of the nominal voltage). In some embodiments, the estimated value during the third stage is determined from the voltage based on a polynomial derived from the characteristic data of the battery discharge.

[0011] In some embodiments, the initial stage can include a first sub - stage and a second sub - stage. During the first sub - stage, the estimated remaining battery capacity is determined from the battery consumption data when it is greater than an upper capacity threshold (e.g., 70% - 80% of the capacity, about 75%). During the second sub - stage, when the estimated capacity is less than the upper capacity threshold, the estimated value is determined from the battery consumption and reaches a lower limit (i.e., takes a minimum value) at a lower capacity threshold (e.g., 40% - 50% of the capacity, about 46%). Also, during the second sub - stage, the voltage can exceed the upper battery voltage threshold. During the initial stage, the remaining battery capacity can be determined as a function of the battery consumption data set (e.g., dead reckoning techniques).

[0012] Another aspect relates to an external device (e.g., a clinical medical programmer, a patient remote control, or other device) configured to determine battery remaining level according to the method described above. In some embodiments, the external device is communicably connectable to an implantable device. The external device includes a graphical user interface configured to facilitate programming and monitoring of the implantable device, and one or more processors operably connected to a memory storing executable instructions for performing the methods described herein. For example, the instructions may be configured to establish communication between the implantable device and the programmer, receive information including battery energy consumption data and the voltage of the battery of the implantable device from the implantable device, and determine an estimated remaining battery level based on the received information. During an initial stage of use, the estimated remaining battery level is determined from the cumulative battery energy consumption relative to the total capacity at full charge. During an intermediate stage of use, the estimated remaining battery level is determined based on a combination of battery energy consumption data and voltage. During a third stage of use, the estimated remaining battery level is determined based on voltage. In some embodiments, the external device is configured to estimate the remaining battery level from the cumulative battery energy consumption during an initial stage and estimate the battery capacity based on voltage during a later stage. In some embodiments, the later stage is immediately after the initial stage, while in other embodiments, the later stage is the third or final stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0013]

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[0014] This application relates to a nerve stimulation therapy system and associated implantable devices and programmer devices, and more particularly, to methods and systems for determining an estimated battery level of a battery for an implantable device. In the exemplary embodiments described herein, the system is a sacral nerve stimulation therapy system configured to treat overactive bladder (OAB) and relieve symptoms of bladder-related dysfunction. However, it is understood that the devices, systems, and methods disclosed herein may also be utilized for various neuromodulation applications such as bowel dysfunction, and for the treatment of other indications such as pain, or movement disorders, mood disorders, or for any implantable medical device powered by a battery.

[0015] A nerve stimulation (or neuromodulation, as may be used interchangeably herein) therapy system, such as any of the nerve stimulation systems described herein, can be used to treat various diseases and related symptoms such as acute painful disorders, movement disorders, mood disorders, bladder-related dysfunction, and the like. Examples of pain disorders that can be treated by nerve stimulation include post-spinal surgery pain syndrome, reflex sympathetic dystrophy or complex regional pain syndrome, causalgia, arachnoiditis, and peripheral neuropathy.

[0016] Movement disorders include paralysis, tremors, dystonia, and Parkinson's disease. Mood disorders include depression, obsessive-compulsive disorder, cluster headache, Tourette syndrome, and certain types of chronic pain. Bladder-related dysfunction includes, but is not limited to, OAB, urge incontinence, urinary urgency frequency, and urinary retention. OAB is one of the most common urinary disorders and is characterized by the presence of bothersome urinary symptoms including urgency, frequency, nocturia, and urge incontinence, and may include any one or a combination of these symptoms.

[0017] Nerve stimulation methods include sacral nerve modulation (SNM). SNM is an established treatment that provides a safe, effective, reversible, and long-lasting treatment option for the management of OAB. SNM therapy involves the use of weak electrical pulses to stimulate the sacral nerves located in the lower back. The electrodes are generally placed adjacent to the sacral nerves, typically at the S3 level, by inserting leads into the corresponding foramina of the sacrum. The leads are inserted subcutaneously and then connected to an implantable pulse generator (IPG), which is also referred to herein as an implantable nerve stimulator or nerve stimulator.

[0018] FIG. 1 schematically illustrates an exemplary nerve stimulation system that includes both a test nerve stimulation system 200 and a permanently implanted nerve stimulation system 100. An external pulse generator (EPG) 80 and an implantable pulse generator (IPG) 10 correspond to a clinical medical programmer 60 and a patient remote control 70, respectively, and communicate wirelessly with the clinical medical programmer 60 and the patient remote control 70, which are used in the positioning and / or programming of the test nerve stimulation system 200 and / or the permanently implanted system 100 after a test is successful. The clinical medical programmer may include dedicated software, dedicated hardware, and / or both to assist in determining the battery remaining estimate, lead placement, programming, reprogramming, stimulation control, and / or parameter setting. In addition, the patient remote control 70 provides at least partial control of the stimulation (e.g., starts a pre-set program, increases or decreases the stimulation) and / or may be used with one or both of the IPG and EPG to monitor the battery status.

[0019] In one aspect, the clinical medical programmer 60 is used by a physician to adjust the EPG and / or the settings of the IPG while the lead is implanted inside the patient. The clinical medical programmer can be a tablet computer used by a clinician to program the IPG during a test period or to control the EPG. The patient remote control 70 can enable the patient to turn the stimulation on or off or to vary the stimulation from the IPG while implanted or from the EPG during the test phase.

[0020] In one embodiment, the clinical medical programmer 60 has a control unit that, in addition to the periodic evaluation of the IPG including the estimation of the battery level described herein, may include a microprocessor and dedicated computer code instructions for implementing the methods and systems used by a physician when deploying the treatment system and setting the treatment parameters. The clinical medical programmer generally includes a user interface, which can be a graphical user interface.

[0021] The electrical pulses generated by the EPG and the IPG are delivered to one or more target nerves through one or more electrodes at or near the distal end of each of the one or more leads. The leads can differ in size, shape, and material and can be tailored for a particular therapeutic use. In the case of the SNM system, the lead is sized and of a length suitable to extend from the IPG through one of the foramina of the sacrum to the target sacral nerve. However, the lead and / or the stimulation program can vary depending on the target nerve.

[0022] Figures 2A-2C show diagrams of the nerve structures of various patients that can be targeted in nerve stimulation therapy. Figure 2A shows various sections of the spinal cord and the corresponding nerves within each section. The spinal cord is a long, thin bundle of nerves and supporting cells that extends from the brainstem, along the cervical spinal cord, through the thoracic spinal cord, to the space between the first and second lumbar vertebrae within the lumbar spinal cord. When exiting the spinal cord, the nerve fibers split into multiple branches, and these multiple branches distribute nerves to various muscles and organs, transmitting sensory and control impulses between the brain, organs, and muscles. Since a nerve can include a branch that distributes nerves to an organ and a branch that distributes nerves to a muscle, when a nerve is stimulated near the nerve root of the spinal cord or in its vicinity, the nerve branches that distribute nerves to the target organ or muscle can be stimulated.

[0023] Figure 2B shows the nerves associated with the lower back within the lower lumbar spinal region where nerve bundles exit the spinal cord and proceed through the sacral foramina of the sacrum. In some embodiments, the lead is advanced through the foramen until the electrode is positioned on the anterior sacral nerve root, while the anchor portion of the lead is generally positioned dorsal to the sacral foramen through which the lead passes to anchor the lead. Figure 2C shows a detailed view of the nerves of the lumbosacral nerve trunk and the sacral plexus, particularly the S1-S5 nerves of the lower sacrum. The S3 sacral nerve is particularly important for the treatment of bladder-related dysfunction, particularly OAB.

[0024] Figure 3 schematically illustrates an example of a fully implanted nerve stimulation system 100 adapted for sacral nerve stimulation. The nerve stimulation system 100 includes an IPG 10 that is implanted within the lower back region and is connected to a lead 20 that extends through the S3 foramen for the stimulation of the S3 sacral nerve. The lead is anchored by a tine anchor portion 30 that maintains the position of a set of nerve stimulation electrodes 40 along the target nerve to provide therapy for various bladder-related dysfunctions, and the target nerve in this example is the anterior sacral nerve root S3 that deactivates the bladder. Although this embodiment is adapted for sacral nerve stimulation, it will be understood that similar systems can be used to stimulate either the target peripheral nerve or the posterior epidural space of the spine for the treatment of other indications.

[0025] In the embodiment illustrated in FIG. 3, the implantable nerve stimulation system 100 includes a controller within the IPG, which has one or more pulse programs, schedules, or patterns that can be pre-programmed or created as described above. In some embodiments, these same characteristics associated with the IPG can be used in the EPG of a partially implanted test system that is used prior to implantation of the permanent nerve stimulation system 100.

[0026] FIG. 4 illustrates an exemplary nerve stimulation system 100 that is fully implantable and adapted for sacral nerve stimulation therapy. The implantable system 100 includes an IPG 10 connected to a nerve stimulation lead 20, which includes a group of nerve stimulation electrodes 40 at the distal end of the lead. The lead includes a lead anchor 30 having a series of tines that extend radially outwardly to anchor the lead and maintain the position of the lead 20 after implantation. The lead 20 may further include one or more radiopaque markers 25 to assist in lead localization and positioning using visualization techniques such as fluoroscopy. In some embodiments, the IPG provides monopolar or bipolar electrical pulses that are delivered to the target nerve generally through one or more electrodes, typically four electrodes. In sacral nerve stimulation, the lead is generally implanted through the S3 foramen as described herein.

[0027] The system may further include a patient remote control 70 and a clinical medical programmer 60, each configured to wirelessly communicate with an IPG implanted during long-term treatment or with an EPG during testing. The clinical medical programmer 60 can be a tablet computer used by a clinician to program the IPG and the EPG. The patient remote control utilizes radio frequency (RF) signals to communicate with the EPG and the IPG and can be a battery-operated portable device that enables the patient to adjust the stimulation level, check the status of the IPG's battery level, and / or turn the stimulation on or off. The IPG can utilize RF polling at various scan rates to facilitate communication sessions with the clinical medical programmer and the patient remote control.

[0028] Figures 5A and 5B show detailed views of the IPG 10 and its internal components. In some embodiments, the pulse generator can generate one or more non-excisional electrical pulses that are delivered to a nerve to produce a desired effect, such as controlling pain or inhibiting, preventing, or interfering with nerve activity for the treatment of OAB or bladder-related dysfunction. In some applications, the pulses can have a pulse amplitude in the range of 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 range or intermediate range of amplitudes can be used. The pulse generator can include a controller (e.g., a processor) and / or memory that is adapted to provide commands to and receive information from other components of the implantable nerve stimulation system. The processor can include a microprocessor such as a commercially available microprocessor from Intel®, Advanced Micro Devices, Inc.®, etc.

[0029] One or more characteristics of the electrical pulse can be controlled by a processor or controller of the IPG or EPG. These characteristics can include, for example, the frequency, intensity, pattern, duration of the electrical pulse, or other aspects of timing and magnitude. In addition, the controller can vary the voltage and current used to generate the pulse. The controller can establish a repeatable pattern or program of pulses applied to the electrodes. For example, the system can provide a selection of a predetermined electrical pulse program, plan, or pattern from one or more available options. In one aspect, the IPG 10 includes a controller, also referred to herein as a processor or microprocessor, that has one or more pulse programs, plans, or patterns that can be created and / or pre-programmed. In some embodiments, the IPG can vary stimulation parameters including pulse amplitude in the range of 0 mA to 10 mA, pulse width in the range of 50 μs to 500 μs, pulse frequency in the range of 5 Hz to 250 Hz, stimulation mode (e.g., continuous or cycle), and electrode configuration (e.g., anode, cathode, or off), and can be programmed to achieve an optimal treatment outcome individualized for the patient. This programming allows for an optimal setting to be determined for each patient, even though each parameter can vary from individual to individual.

[0030] As shown in FIGS. 5A and 5B, the IPG10 may include a header portion 11 at one end. The header portion 11 houses a feed-through assembly 12, a connector stack 13, and a communication antenna 16 for facilitating wireless communication with a clinical medical programmer and / or a patient remote control. The IPG10 includes a case 17, and the case 17 houses a circuit configuration 23 including a printed circuit board, a memory, and controller components for facilitating the above-described electrical pulse program. A battery 24 is also housed within the case 17. In some embodiments, the battery is a non-rechargeable primary battery. In some embodiments, the battery is a lithium manganese dioxide (Li-MnO2) battery (e.g., the Litronik LiS 3150 MK battery with a capacity of 1200 mAh and a nominal voltage of 3.1V). However, it will be understood that any suitable battery may be used as needed for a particular application.

[0031] FIG. 6 shows a schematic diagram of one embodiment of the architecture of the IPG10. In some embodiments, each component of the architecture of the IPG10 may be implemented using a processor, a memory, and / or other hardware components of the IPG10. In some embodiments, the components of the architecture of the IPG10 may include software that interacts with the hardware of the IPG10 to achieve a desired result, and the components of the architecture of the IPG10 may be located within the housing.

[0032] In some embodiments, the IPG10 may include, for example, a communication module 600. The communication module 600 may be configured to transmit and receive data, for example, with other components of an exemplary nerve stimulation system including a clinical medical programmer 60 and / or a patient remote control 70, and / or with a device. In some embodiments, the communication module 600 may include one or more antennas and software configured to control the one or more antennas to transmit and receive information between the one or more other components of the IPG10.

[0033] IPG10 may further include a data module 602. The data module 602 may be configured to manage data related to the identity and characteristics of the IPG10. In some embodiments, the data module 602 may include one or more databases that may contain information related to the IPG10 stored, for example, in a memory device. This information may include, for example, an identifier of the IPG10, or one or more characteristics of the IPG10. In one embodiment, the information related to the characteristics of the IPG10 may include, for example, data specifying the functions of the IPG10, past stimulation program data, the power consumption of the IPG10, and battery consumption data, which may include one or more types of battery usage data, cumulative battery consumption values (plural available), data specifying the charging capacity of the IPG10, and / or the power storage capacity of the IPG10, and various monitoring parameters including battery voltage measurement values.

[0034] IPG10 may include a pulse control 604. In some embodiments, the pulse control 604 may be configured to control the generation of one or more pulses by the IPG10. In some embodiments, for example, this may be performed based on information specifying one or more pulse patterns, programs, etc. This information may further specify, for example, the frequency of the pulses generated by the IPG10, the duration of the pulses generated by the IPG10, the intensity of the pulses generated by the IPG10, and / or the magnitude, or any other details related to the generation of one or more pulses by the IPG10. According to various exemplary embodiments, this information may specify aspects of the pulse pattern and / or pulse program, such as the duration of the pulse pattern and / or pulse program. In some embodiments, the information related to the pulse generation of the IPG10, and / or the information for controlling the pulse generation of the IPG10, may be stored in the memory.

[0035] In some embodiments, the pulse module 604 may include a stimulation circuitry mechanism. The stimulation circuitry mechanism may be configured to generate and deliver one or more stimulation pulses, specifically, may be configured to generate a voltage that drives a current that forms one or more stimulation pulses. This circuitry mechanism may include one or more various components that can be controlled to generate one or more stimulation pulses, control one or more stimulation pulses, and / or deliver one or more stimulation pulses.

[0036] The IPG 10 includes an energy storage device 608 such as a battery. In the embodiments described herein, the IPG 10 is powered by a primary battery (e.g., a non-rechargeable battery). Over the life of the battery, the battery capacity and voltage are consumed by various types of usage (e.g., delivery of stimulation pulses, self-discharge, energy consumption during communication with an external device, etc.). As the battery capacity is consumed, the amount of energy available for the functions of the IPG 10 decreases over time. The depletion of the battery capacity can vary depending on treatment parameters, patient-specific usage, and other various factors. The depletion of the battery capacity can also be affected by a plurality of factors including stimulation frequency, lower battery voltage, communication frequency, etc., and while other factors (e.g., self-discharge, standard RF communication polling) can cause a depletion of the battery capacity at a nearly constant rate, the above-mentioned plurality of factors can have various effects on or accelerate the depletion of the battery capacity. The battery life can vary significantly depending on the various usage factors described above.

[0037] Many systems rely on voltage monitoring and determine an estimate based on a standard battery discharge curve or look-up table. However, as can be seen from FIG. 7, the voltage plot 700 of the battery discharge curve is non-linear and can be highly variable. As shown in the figure, there is an initial steep drop, followed by a stable rise over most of the battery's life, after which the voltage begins to decline steadily and then may drop exponentially towards the end of the battery's life. Therefore, the approach of determining battery capacity based on voltage is relatively inaccurate over most of the battery's life, and when the voltage drops significantly, only a short time remains until the end of life and the battery must be replaced. Other approaches have attempted to determine battery capacity by estimating battery discharge and subtracting these discharge estimates from the battery capacity at the start of use, but these approaches inherently contain errors. Over time, these errors cause the estimated battery capacity to become increasingly inaccurate, which can result in the battery being replaced at too early a timing or not providing sufficient notice of the end of battery life. Furthermore, due to the fundamental differences between these various approaches, the estimated battery capacities from these approaches can be quite different, and as a result, clinicians and patients cannot reasonably rely on the estimated battery remaining. Therefore, there is still a need for improved methods and systems that provide more accurate and reliable estimates of battery remaining to patients and clinicians. Based on the estimated battery remaining, if the battery unexpectedly depletes earlier than expected, the patient and clinician can be notified, and as a result, the IPG can be replaced at an appropriate timing. However, determining the battery remaining is not an easy task.

[0038] As described herein, the battery charge may be a function of the rated battery capacity provided by the manufacturer, the design parameters of the system (e.g., the stimulation circuit configuration including the hardware and software of the IPG10), the patient's impedance, the programmed stimulation parameters (e.g., current, pulse width, frequency, ramp duration, cycling, number of cathodes, etc.), or other parameters described herein.

[0039] FIG. 7 illustrates an example of a battery discharge curve 700 showing battery voltage as a function of battery capacity over the battery's useful life (i.e., period of use). The curve shown is for a Li-MnO2 primary battery. It will be understood that various other types of batteries, particularly lithium-based primary batteries, have similar discharge curves. In this discharge curve, after an initial period where the voltage remains relatively constant (except for an initial rapid rise, rapid fall, and slow rise), the battery voltage begins to steadily decline as the battery capacity decreases. The overall rate at which the battery discharges can vary depending on the degree of use over time. During this initial period, which is typically from 4 to 6 years, the voltage remains relatively constant and rises slowly, so monitoring the voltage during this period to determine battery charge is generally not accurate. However, by using an approach that takes into account the various types of battery usage that occur during this period relative to the battery capacity at the start of use, a relatively accurate estimate of the battery charge can be provided. As the battery approaches the end of its life and the voltage steadily declines, monitoring the voltage becomes a better indicator of battery charge because the battery voltage can correspond to the characteristic discharge curve of each battery. In the case of this battery, this steady decline typically lasts for about 1 to 2 years, after which, in a later stage that lasts for about 3 years, a more rapid decline and rapid fall occur. However, during the intermediate period between this initial and later stage, neither the first consumption-based method nor the second voltage-based approach is completely accurate. Furthermore, because there are fundamental differences between these approaches, significantly different estimates can be obtained by each approach, and neither estimate may be reasonably reliable.

[0040] To overcome the above dilemma and provide a more reliable and consistent estimation of battery remaining capacity over the entire life of the battery, the methods and systems of this specification use a method of dividing battery discharge into at least various stages, as shown in FIG. 7. These various stages include an initial stage PH1, an intermediate stage PH2, and a third stage PH3.

[0041] During the initial stage PH1, the curve 700 may be relatively flat at or near the nominal voltage. After an initial rapid rise and fall, the voltage remains relatively stable and shows only a slight increase during this period. When the voltage exceeds the upper battery voltage threshold V upper , the voltage begins to steadily decrease. In some embodiments, V upper corresponds to about 95% - 99% of the nominal battery voltage at full battery capacity, and is typically about 96% (e.g., 2.974V for a battery with a nominal voltage of 3.1V). In the illustrated example, for a battery with a nominal voltage of 3.1V, the battery voltage at full charge varies between 2.94V and 3.3V, and V upper is about 2.974V. While the voltage is above V upper , for example, during the initial stage PH1, the remaining battery capacity can be estimated to be greater than the lower battery capacity threshold BC lower (e.g., 45% of full capacity). In some embodiments, during the initial stage PH1, an accurate prediction of the battery capacity can be made based on energy consumption data regarding the energy used by the battery under various loads over the elapsed usage period. It has been observed that at voltages below the upper battery voltage threshold V upper , the remaining battery capacity can gradually be estimated as a function of the battery voltage. For this reason, the method predicts the remaining battery capacity using an equation when below this threshold V upper , and this equation can be derived at least in part as a function of the battery voltage.

[0042] The threshold Vupper and the lower battery voltage threshold V lowerDuring the intermediate stage PH2 that occurs between them, the voltage steadily decreases, and the remaining battery capacity can be determined as a linear function of the voltage. Threshold voltage V lower can be a voltage between 85% and 95% of the battery voltage at full capacity (for example, about 93%, 2.87V for a battery with a nominal voltage of 3.1V). However, as described above, the estimated value determined based on the battery consumption at threshold voltage V upper may not match the estimated value based on the voltage at threshold voltage V upper Therefore, in some embodiments, to provide a more consistent estimate of the remaining battery capacity, the method includes an equation that blends a first estimated value based on battery consumption with a second estimated value based on voltage. These estimated values can be linearly blended with a first estimated value fully weighted at threshold voltage V upper and a second estimated value fully weighted at threshold voltage V lower .

[0043] During the third stage PH3 when the voltage is below threshold voltage V lower , the remaining battery capacity can be predicted based only on the voltage without using battery consumption data. In some embodiments, a polynomial function of the voltage is used for the determination. In some embodiments, this polynomial function can approximate a curve that follows the battery characteristic data of a certain battery type.

[0044] FIG. 8 shows a battery discharge curve as a voltage plot 710 with the axes swapped to show the remaining battery capacity as a function of the voltage (e.g., as a percentage of full capacity). FIG. 8 shows various different stages and sub-stages of the curve that can be used in the method for estimating the remaining battery capacity. Further details, parameters, and equations of the method will be described in more detail below.

[0045] During the initial stage PH1 shown in FIG. 8, after the initial rapid rise and fall, the voltage slightly increases, and as a result, the same voltage appears at different times, which may make estimation difficult. Therefore, the initial stage PH1 can be divided into a first sub-stage PH1-1 and a second sub-stage PH1-2. The first sub-stage PH1-1 distinguishes between the low battery voltage seen at an early stage of the battery life and the same low battery voltage seen at a later stage of the battery life. Therefore, different equations can be used for the first sub-stage PH1-1 and the second sub-stage PH1-2 to estimate the remaining battery capacity. The following description shows an overview of the process for estimating the remaining battery capacity for various stages observed during the use period of an embeddable device (e.g., IPG).

[0046] In one aspect, at the initial stage of battery use, the remaining battery capacity can be estimated using a first consumption-based method (e.g., dead reckoning) that tracks or takes into account the battery energy consumed over the elapsed usage period. For example, the total battery energy consumption related to the energy use by various IPG electrical loads can be calculated and subtracted from the battery capacity of a new battery (e.g., the rated capacity provided by the manufacturer) to estimate the remaining battery capacity. At a later stage, the remaining battery capacity can be estimated using a second method based at least in part on the voltage of the battery.

[0047] The first consumption-based method (e.g., dead reckoning) can accurately predict the remaining battery capacity when the actual battery energy consumption for various usage methods (e.g., self-discharge, communication with external devices, delivery of stimulation pulses, etc.) is accurately tracked. However, since the battery energy consumption for various usage methods may be based on conservative assumptions, the first method may overestimate the past battery usage. Therefore, when the voltage drops below a threshold V upper (e.g., 2.974 V) and the estimated remaining battery capacity is below the lower threshold BC of the battery capacity lowerIf it is determined that it is below (e.g., 40 - 50%, about 46%), the first method is likely to predict the remaining battery level overly conservatively (lower than actual). To avoid discontinuity in the estimated remaining battery level, when the battery voltage first reaches V upper (e.g., 2.974V), and / or when the upper threshold of battery capacity BC upper (e.g., 70 - 80%, 75%) or less, the estimated value obtained by the first consumption-based approach can have the lower threshold of battery capacity BC lower (e.g., 40 - 50%, about 46%). When the voltage subsequently drops below V upper , if the remaining amount below the lower limit is predicted by the inference algorithm, then thereafter, the value of the remaining amount is calculated by the second voltage-based method. The second method can estimate the remaining battery level as a function that is at least partially a function of the battery voltage instead of the battery energy consumed by the IPG.

[0048] The process of estimating the remaining battery level can also consider scenarios where the first method may be overly optimistic. In that case, the remaining battery level can be determined by a combination or blending of the results from the first method and the second method. In some embodiments, this blending or combination is applied during the intermediate stage PH2. In FIG. 8, the intermediate stage PH2 is a transition region characterized by a battery voltage between threshold V upper and threshold V lower (e.g., between 2.974V and 2.870V). In some embodiments, the blending is performed such that the remaining battery level determined by the first method predominates near threshold V upper (e.g., 2.974V), and the remaining amount determined by the second method predominates near threshold V lower (e.g., 2.870V).

[0049] In some embodiments, an unexpected battery depletion rate may occur. For example, due to a failure of the power system (such as a battery, circuit, etc.) within the IPG, the battery depletion rate may become unexpectedly fast. In such a situation, the battery voltage will drop much earlier than estimated based on the first method or a combination of the first and second methods. Therefore, in the third stage PH3, which is characterized by the battery being depleted before the battery voltage drops below a threshold Vlower (for example, 2.870V), the remaining amount may be based only on the measured battery voltage. In other words, the battery remaining amount calculated using the first method (such as dead reckoning) may be ignored at this stage. Therefore, if the battery unexpectedly depletes earlier than expected, the patient will be notified of this regardless of the calculations based on the usage of the implantable device.

[0050] Various operations and methods that use battery power and deplete the battery will be described below. All or part of these battery power usage methods may be considered when determining the estimated battery remaining amount based on battery depletion data. Such usage methods may include the following.

[0051] In some embodiments, the fixed battery energy usage dataset 921 includes a first energy consumption dataset regarding the battery energy (usage amount 1) consumed or lost due to the self-discharge of the battery. The self-discharge of the battery may be the amount of energy consumed inside the battery. Since self-discharge occurs inside the battery, the first usage amount may not be measurable outside the battery. In some embodiments, the parameters used to determine the first energy consumption dataset may include, but are not limited to, the annual self-discharge rate (for example, in percentage), the rated capacity of the battery (for example, in joules), or other related factors. For example, the self-discharge of the battery may be calculated in joules per day as a function of the rated capacity and the self-discharge rate.

[0052] In some embodiments, the fixed battery energy usage dataset 921 includes a second energy consumption dataset regarding the battery energy (usage 2) consumed by communication polling at a first scan rate to determine whether an external device is requesting communication. In some embodiments, the second energy consumption dataset includes the amount of battery energy consumed during a periodic scan performed by the IPG to determine whether an external device (e.g., remote control 70 or clinical medical programmer 60) is requesting communication with the IPG. The energy consumption over time depends on several factors and may be difficult to model analytically. Thus, the battery energy consumption can be determined using parameter values from actual measurements. The parameters used to determine the second energy consumption dataset may include, but are not limited to, the energy used from the battery to receive a radio frequency (RF) signal (e.g., in microjoules per scan), an energy margin provision for a potential future increase in energy required per scan (e.g., in microjoules per scan), the modeled energy drawn from the battery to receive an RF signal (microjoules per scan), the RF scan interval (e.g., in seconds), the number of RF scans per day, or other related parameters. Thus, the second energy consumption dataset may include the total polling energy calculated as a function of the aforementioned parameters.

[0053] In some embodiments, the fixed battery energy usage dataset 921 includes a third energy consumption dataset regarding the battery energy (usage 3) consumed by radio frequency communication including communication with an external device. In some embodiments, the third energy consumption dataset includes the amount of energy consumed when the IPG (e.g., 10) is executing a communication episode with an external device (e.g., remote control 70 or clinical medical programmer 70). The energy consumption over time depends on several factors and may be difficult to analytically model. Thus, the model parameters of an analytical model or an empirical model can be determined using values from actual measurements during various stages of communication, and the total value of the energy consumption can be calculated. For example, the parameters used to determine the third energy consumption dataset may include the energy (e.g., measured in millijoules) consumed for a given amount (e.g., in seconds) of a call, the energy margin provided for future changes (e.g., assumed in millijoules), the number of calls per episode, the number of episodes per year, the daily scan allocation number, or other factors, but are not limited thereto. Thus, the third energy consumption dataset may include the total daily allocated energy (e.g., in joules per day) that can be calculated as a function of the aforementioned parameters.

[0054] In some embodiments, the fixed battery energy usage dataset 921 includes a fourth energy consumption dataset regarding the battery energy (usage 4) consumed by communication at a second scan rate to determine whether an external device is requesting communication. In some embodiments, the implantable device may be configured to poll for follow-up calls with an external device at a second scan rate. For example, after an RF call session, for a subsequent predetermined period (e.g., 60 seconds), the IPG may poll for follow-up calls with the patient remote or clinical medical programmer at a faster rate (e.g., 4.6 second period) compared to the normal polling rate (e.g., 16 second period). Such a feature may be intended to enhance the responsiveness of the IPG to immediate follow-up calls. Such a communication model may be developed using parameters related to the energy usage from RF polling within the second energy consumption dataset. Further, the parameters used to determine the fourth energy consumption dataset include, but are not limited to, the second scan interval (e.g., seconds), the duration after interaction with the external device (e.g., in minutes), the number of scans per episode, the energy consumed from the battery to receive an RF signal (e.g., in microjoules per scan), the energy consumed from the battery per episode to receive an RF signal (e.g., in millijoules), the energy per episode for memory writing (e.g., in millijoules), the number of episodes per year, the daily fraction of episodes, or other parameters. Accordingly, the fourth energy consumption dataset may include the total daily fraction RF communication polling energy in joules per day calculated as a function of the aforementioned parameters.

[0055] In some embodiments, the fixed battery energy usage dataset 921 includes a fifth energy consumption dataset regarding the battery energy (usage 5) consumed by housekeeping tasks executed by software within an implantable device. In some embodiments, the fifth energy consumption dataset includes the energy usage for numerous housekeeping functions that the IPG software periodically executes. Such energy usage can be modeled. The values used in this model can be determined from measurement data. A sufficient margin relative to the measured values can be modeled, taking into account the possibility that additional housekeeping tasks may be added in future software versions and more energy may be used. The parameters used to determine the fifth energy consumption data may include, but are not limited to, the energy consumed from the battery for housekeeping tasks (e.g., in joules per day), an energy margin considering the potential increase in future energy usage associated with software changes, or other parameters. Accordingly, the fifth energy consumption data can be the total energy in joules per day calculated as a function of the aforementioned parameters.

[0056] In some embodiments, the fixed battery energy usage dataset 921 includes a sixth energy consumption dataset regarding the battery energy (usage 6) consumed by the quiescent current present within the implantable device. For example, the sixth energy consumption dataset includes the amount of energy used from the battery to provide the quiescent current that the IPG constantly consumes after the battery is connected to the IPG's circuit board. Parameters used to determine the sixth energy consumption dataset may include, but are not limited to, the system quiescent current removed from the battery (e.g., in μA), the voltage at which quiescent current discharge occurs (e.g., in V), or other parameters. Thus, the sixth energy consumption dataset can be the total energy in joules per day calculated as a function of the aforementioned parameters. In some embodiments, the total energy consumed per day from the battery (e.g., in joules per day) can be calculated as the sum of the first through sixth energy consumption datasets.

[0057] In some embodiments, the fixed battery energy usage dataset 921 includes a seventh energy consumption dataset regarding the energy (usage amount 7) discharged from or lost from the battery due to self-discharge during inventory that the battery experiences before connecting to an implantable device such as the IPG10. In some embodiments, the self-discharge period of the IPG battery can be divided into the following stages: (i) the self-discharge of the battery that occurs from the moment the battery is manufactured; (ii) when the battery is attached, the IPG circuit experiences daily energy consumption (e.g., the first to sixth energy consumption datasets); (iii) after assembly, the IPG is tested at the IPG assembly factory; (iv) after sterilization, the packaged IPG is tested at another manufacturing factory. The periods of various stages during the shelf life can be estimated. The parameters used to determine the seventh energy consumption data may include, but are not limited to, a first period characterized by the time (e.g., in months) from battery manufacture to attachment to the IPG circuit, a second period characterized by the time (e.g., in months) from battery attachment to IPG assembly, a third period characterized by the time (e.g., in months) from IPG assembly to completion of sterilization and preparation for shipment, and a fourth period characterized by the time (e.g., in months) from completion of preparation for shipment to implantation. Thus, the total period from battery manufacture to implantation (e.g., in months) can be calculated as the sum from the first period to the fourth period. Similarly, the period from battery attachment to the circuit to implantation can be calculated as the sum from the second period to the fourth period. Based on the periods (e.g., the first to fourth periods) and the battery self-discharge parameters (e.g., in joules determined in the first energy consumption dataset), the amount of energy consumed from the battery during various periods can be determined.

[0058] In addition to the battery energy consumed during the first through fourth periods, an energy margin (e.g., in joules) that enables an increase in the future upgrade of the test fixture may be included. Further, the energy (e.g., in joules) used during the testing of the packaged IPG at the IPG manufacturer may be included. Based on the aforementioned energy calculations, the total energy used during storage may be calculated as the sum of the energy consumed during the first through fourth periods, the energy margin, or other parameters. These aforementioned energy values regarding battery discharge during storage may be included in the seventh energy consumption data set.

[0059] In some embodiments, the fixed battery energy usage data set 921 includes an eighth energy consumption data set regarding the battery energy (usage 8) consumed by communication with an external device during the implantation of the implantable device. For example, the IPG 10 communicates with the clinical medical programmer 70 during implantation. After implantation, there is usually a follow-up visit by a clinician. After the IPG is implanted, it may also be expected that the patient will visit the clinician's office several times for mid-term examinations or end-of-life examinations of the IPG. The amount of energy used by the IPG when communicating with the clinical medical programmer is approximated by the amount of energy used by the IPG when communicating with the patient remote control. The energy usage of one communication session between the IPG and the patient remote control may be modeled as a predetermined period (e.g., 30 seconds) of the communication session.

[0060] The parameters used to determine the eighth energy consumption data may include, but are not limited to: (i) the total energy consumed (e.g., in millijoules) in one call with a first external device (e.g., a patient remote control) over a predetermined time period (e.g., 30 seconds); (ii) the number of equivalent calls made between the first external device (e.g., a patient remote control) and a second external device (e.g., a clinical medical programmer) during a second period of the implant surgery (e.g., 45 minutes); (iii) the number of equivalent calls made between the first external device (e.g., a patient remote control) and the second external device (e.g., a clinical medical programmer) during a third period of the follow-up surgery (e.g., 10 minutes); (iv) the number of equivalent conversations made between the first external device (e.g., a patient remote control) and the second external device (e.g., a clinical medical programmer) during the IPG mid-term follow-up (e.g., 10 minutes); (v) the number of equivalent calls made between the first external device (e.g., a patient remote control) and the second external device (e.g., a clinical medical programmer) during the IPG end-of-life follow-up (e.g., 10 minutes); and / or (vi) the number of equivalent calls made between the first external device (e.g., a patient remote control) and the second external device (e.g., a clinical medical programmer) during the usage period of the IPG. The above parameters (i) to (vi) and the energy consumption related to these parameters described herein may be used to estimate the energy consumption of the eighth energy consumption data set. For this reason, the fixed energy consumption data set 921 may include a plurality of different types of energy consumption data and related parameters.

[0061] Similarly, the active battery energy consumption dataset 922 includes data regarding parameters that affect the amount of energy required to deliver the stimulation pulses. The amount of energy consumed from the battery depends on parameters related to stimulation pulse delivery (e.g., stimulation pulse frequency, current amplitude, etc.), hardware and software components, characteristics of the patient tissue, or other parameters. In some embodiments, the energy associated with stimulation pulse delivery can be determined using a system model of an implantable device (e.g., IPG10).

[0062] In some embodiments, a model of the IPG hardware and software can be developed to determine the energy consumption associated with the delivery of stimulation pulses. For example, the energy model can be a function of the energy consumption by a central processing unit (CPU) in which the IPG software resides and another energy consumption by an analog circuit used to generate the stimulation pulses. The energy consumption of the CPU can be characterized by a ninth energy consumption dataset, and the hardware energy consumption can be characterized by a tenth energy consumption dataset, both of which are described in more detail below.

[0063] In some embodiments, the ninth energy consumption dataset includes parameters regarding the battery energy (usage 9) consumed by a processor, controller, or central processing unit (CPU) for the emission or activation of stimulation pulses. The parameters related to the CPU energy consumption may have a weak relationship with the duration of the pulses. Also, the CPU energy consumption may not depend on the energy used for the delivery of the stimulation pulses. Thus, the determined CPU energy consumption can be regarded as a fixed amount of energy used for each stimulation pulse.

[0064] The parameters used to determine the ninth consumption data may include, but are not limited to, CPU startup time (microseconds) and CPU startup current (mA). The energy consumption per pulse (microjoules per pulse) for the activation of each stimulation pulse can be calculated as a function of the CPU startup time and current. Further, the parameters may include, but are not limited to, CPU processing time (microseconds) and CPU processing current (mA). The energy consumption during CPU processing (microjoules per pulse) can be calculated as a function of the processing time and current.

[0065] In some embodiments, the stimulation pulses can be delivered in stages. In this case, the parameters may include, but are not limited to, the expected duration of the first stage (microseconds) and the CPU current (mA) during the first stage of stimulation pulse delivery. The energy consumed during the first stage of stimulation pulse delivery (microjoules per pulse) can be calculated as a function of the duration and current of the first stage. Further, the parameters may include, but are not limited to, the inter-stage delay (microseconds), the expected duration of the second stage (microseconds), the CPU current during the inter-stage delay, and the second stage delivery current (mA). The energy consumed during the inter-stage delay and between the second stages (microjoules per pulse) can be calculated as a function of the aforementioned parameters. Therefore, the CPU energy consumption can be estimated using the aforementioned parameters and the CPU energy consumption associated with these parameters. For this purpose, the ninth consumption data set may include parameter values related to startup, processing, various stages, and the corresponding energy consumption. The total CPU energy consumption can also be calculated as the sum of the individual energy consumptions (microjoules per pulse) during startup, processing, and various stages, and may be included in the ninth energy consumption data set.

[0066] In some embodiments, the tenth energy consumption data set includes parameters related to the battery energy (usage 10) consumed to deliver the stimulation pulses by the analog circuits of the IPG. The parameters used to determine the tenth energy consumption data set can include, but are not limited to, the specified stimulation current amplitude, the specified stimulation pulse width, the specified stimulation frequency (e.g., 14 Hz), the resistive portion of the patient (e.g., tissue resistance), the specified duty ratio, and the number of pulses per day.

[0067] The parameters of the tenth energy consumption data set can include the maximum possible current (mA) used to deliver the stimulation pulses. The voltage applied to the patient can be limited to a specific voltage (e.g., 9 volts). A double-layer capacitor (e.g., 0.47 μF) at the electrode / tissue interface can accumulate voltage during the first stage of the stimulation pulse. Thus, the maximum current deliverable to the patient can depend on the patient's impedance and pulse duration. Accordingly, the maximum possible current can be calculated as a function of the specific voltage, the patient's impedance and resistance, and the pulse duration.

[0068] The parameters of the 10th energy consumption data set can include a limited stimulation current amplitude (μA) used for the delivery of the stimulation pulse, and this limited stimulation current amplitude can be the lower limit value between the programmed current and the maximum possible current calculated above. The parameters of the 10th energy consumption data set can include the stimulation voltage (mV) at the resistive part of the patient, and this stimulation voltage can be the voltage at the start of the first stage assuming that the resistive part of the patient is purely resistive. The parameters of the 10th energy consumption data set can include the voltage accumulation in the part of the patient that functions as a capacitor during the delivery of the first stage of the stimulation pulse. The parameters of the 10th energy consumption data set can include the voltage drop due to the switch, sense resistor, and system circuit impedance. The voltage can drop internally within the IPG according to the current due to the impedance of the internal components. The parameters of the 10th energy consumption data set can include the total stimulation voltage required from the stimulation power source, and can be calculated as the sum of the stimulation voltage, voltage accumulation, and voltage drop.

[0069] In some embodiments, the hardware components of the IPG may include a power converter such as a buck power supply (e.g., to lower voltage) and / or a boost power supply (e.g., to raise voltage). Accordingly, the parameters of the tenth energy consumption data set may include a minimum buck specification and a maximum boost specification. The voltage used to deliver the stimulation pulse may be limited. The lower limit may correspond to the minimum voltage that the buck power supply can provide. The upper limit may correspond to the maximum voltage that the boost power supply can provide. In some embodiments, either the buck power supply or the boost power supply operates in response to the voltage demand during stimulation pulse delivery. Further, the parameters of the tenth energy consumption data set may include the efficiency of the power converter, which may depend on bucking and / or boosting. The parameters of the tenth energy consumption data set may include the amount of energy supplied by the stimulation power source during the first stage. The energy supplied may be calculated as a function of the stimulation current, stimulation voltage, and the duration of the first stage. The parameters of the tenth energy consumption data set may include the energy (in microjoules) consumed from the battery per pulse, based on the efficiency of the buck converter and / or boost converter.

[0070] The parameters of the 10th energy consumption dataset may include the quiescent current (μA) consumed by the buck converter and / or the boost converter. For example, in the case of a buck converter, the quiescent current persists throughout the duration of the stimulation pulse. On the other hand, in the case of a boost converter, the quiescent current persists only during the duration of the first stage. Further, the parameters may include the quiescent current energy per pulse (microjoules), which can be calculated as a function of the quiescent current(s) of the converter, the duration of the quiescent current(s), and the stimulation frequency. The parameters of the 10th energy consumption dataset may include the total analog circuit energy, which can be calculated as the sum of the energy (microjoules) consumed from the battery per pulse and the quiescent converter energy per pulse. Thus, the active battery energy consumption dataset 922 may include the total energy associated with the delivery of the stimulation pulse, which can be calculated as the sum of the energy consumption associated with the digital circuit (e.g., CPU) and the analog circuit for the delivery of each stimulation pulse.

[0071] In some embodiments, based on the battery energy consumption dataset and related parameters, the life or expected life of the battery (e.g., in days) can be calculated. In one aspect, the battery life can be calculated by the following formula: Life (days) = Usable battery capacity / Daily usage Usable battery capacity = Derated battery capacity - (Usage7 + Usage8) Daily usage = (Usage1 to Usage6) + (E * F * C) E = Energy factor = (Usage9 + Usage10) * (Number of pulses per day at 14 Hz) F = Frequency factor = Stimulation frequency / 14 C = Cycling factor (C = 1 if cycling is not used) =(Cycling_On_Time_Secs+5*Ramp_Time_Secs) / (Cycling_On_Times_Secs+Cycling_Off_Time_Secs)

[0072] As shown above, the battery life can be the ratio of the available battery capacity to the daily energy consumption. The available battery capacity can be calculated as the difference between the derated battery capacity and the sum of the total energy calculated in the seventh and eighth energy consumption data sets. The daily energy consumption can be calculated as a function of the total energy in the first through sixth energy consumption data sets, the energy factor (E), the frequency factor (F), and the cycling factor (C). For example, the energy factor (E) can be a function of the total energy in the ninth and tenth energy consumption data sets and the number of pulses at a given frequency (e.g., 14 Hz per day). The frequency factor (F) can be a function of the stimulation frequency and a given frequency (e.g., 14 Hz). The frequency factor (F) takes into account the effect of energy due to the use of a stimulation frequency other than the given frequency (e.g., 14 Hz). The cycling factor (C) takes into account the cycling through the start, stop, and ramping of the stimulation that uses extra energy. This can be taken into account by multiplying the ramp time when calculating the cycling factor (C).

[0073] FIG. 9 is an exemplary flowchart of a method 900 for determining an estimated remaining battery charge in an implantable device over a usage cycle. Method 800 includes steps of estimating the remaining battery charge based on battery energy consumption and battery voltage at various stages of battery life. In some embodiments, the battery can be a non-rechargeable primary battery of the IPG 10. For example, the battery can be a lithium manganese dioxide (Li-MnO2) battery. Accordingly, method 800 can include steps of collecting and analyzing data regarding battery energy usage associated with the IPG, measuring the battery voltage from the IPG, and estimating the remaining battery charge using the data.

[0074] Most of the battery energy consumed by the IPG can be used after power conversion to a fixed voltage. In other words, most of the energy consumed can be independent of the battery voltage. As the remaining battery charge depletes, the battery voltage decreases and drops sharply (as described, for example, with respect to FIGS. 7-8). For a given amount of energy, as the battery depletes, more current is drawn from the battery. Accordingly, the energy supply capacity of the battery (e.g., in joules) can be prioritized over the current supply capacity (amperes) of the battery. Accordingly, method 800 can determine the battery capacity with respect to energy (e.g., in joules), even if the battery manufacturer evaluates the battery with respect to the current supply capacity (mAh) of the battery. Although this method determines the capacity with respect to energy (e.g., in joules), this method can also be applied and converted to measure the capacity with respect to watt-hours, milliampere-hours, coulombs, or a percentage of the rated or maximum capacity. Method 900 will be described in more detail below.

[0075] In exemplary method 900, step 902 includes establishing communication between an implantable device and an external device. In some embodiments, the implantable device can be an IPG for any nerve stimulation therapy (e.g., for the brain, muscle, spinal cord, sacrum, etc.). In some embodiments, the external device can be a clinical medical programmer and / or a remote control (e.g., the clinical medical programmer 60 and / or the patient remote control 70 of FIG. 1), and / or any other electronic device capable of communicating with the IPG. The clinical medical programmer 60 can send and receive commands for the operation of the IPG (e.g., activation of the IPG, delivery of stimulation pulses having specific frequencies and energies), and communicate with the IPG to facilitate telemetry regarding the performance or operating parameters of the IPG (e.g., receipt of battery consumption data, receipt of battery usage, past stimulation program data / settings, etc.). The remote control 70 can be configured to communicate and control parameters related to the delivery of stimulation pulses of the IPG. In some embodiments, a user (e.g., a patient) can change the stimulation parameters set by the clinical medical programmer 60 to deliver higher or lower stimulation pulses. Thus, the actual battery usage may be higher or lower compared to the estimated battery usage of the program set by the clinical medical programmer 60.

[0076] Step 904 includes receiving, from an implantable device (e.g., IPG10), information including battery energy consumption data BC920 and battery voltage V930. In some embodiments, the battery energy consumption data 920 includes data related to various energy consumptions of the battery that deplete the battery capacity. In some embodiments, the battery energy consumption data includes fixed battery energy usage data 921 related to a fixed amount of energy consumption from the battery for a particular purpose. In some embodiments, the battery energy consumption data set includes active battery energy consumption data 922 related to the delivery of stimulation pulses. The fixed battery energy usage data set may include, but is not limited to, any of battery energy consumption related to fixed battery discharge, periodic communication with an external device, housekeeping tasks related to the functions of software in the implantable device, quiescent current consumed by the implantable device, or any combination thereof.

[0077] Step 906 includes determining an estimated remaining battery level based on the received information. In some embodiments, the estimated remaining battery level may be determined in different ways for various stages observed during the usage period. In some embodiments, in step 906, the estimated remaining battery level is determined as shown in FIG. 10 and will be described in more detail below. Thereafter, the external device outputs an estimated value 940 of the battery capacity to the user, for example, on a graphical user interface display.

[0078] In some embodiments, communication is established during a clinician's examination of a patient, at which time the clinician programmer queries the IPG (e.g., by direct communication between the IPT and the clinician programmer) to determine an estimated battery level. The estimated value can be expressed in any suitable way (e.g., as a percentage of total capacity, or as remaining battery life in units such as weeks / months / years). In some embodiments, the clinician programmer determines an estimated value of the battery capacity from the information obtained in the session and presents that estimated value to the clinician, but otherwise does not store or retain the estimated value in the clinician programmer or patient profile. Since the estimated value is not stored or retained, the clinician programmer cannot update the estimated value. In such embodiments, when a new or updated estimated value is needed, the clinician programmer needs to start a new communication session, receive updated data, and perform a new estimate according to the methods detailed herein. In some embodiments, the estimated value of the battery capacity can be determined and / or displayed on a patient remote control or other electronic device that can communicate with the IPG.

[0079] Figure 10 shows an exemplary method 1000 for determining an estimated remaining battery level according to some embodiments. The method includes steps 1001, 1002, and 1003 corresponding to different stages (e.g., an initial stage, an intermediate stage, and a third stage) during the usage period over the battery's lifespan. In some embodiments, each of steps 1001, 1002, 1002 is not executed simultaneously or in sequence, but rather at different times. In some embodiments, steps 1001, 1002, 1003 may be selectively executed based on trigger conditions defined as a function of battery voltage and / or remaining battery level according to the voltage and capacity thresholds described herein. Step 1001 is executed during the initial stage of use, and in this step 1001, the estimated remaining battery level is determined from a battery energy consumption data set relative to the full charge battery capacity when the battery is first used. This full charge capacity value provides a more conservative approach for estimating the battery capacity and may be dilated to ensure sufficient notice of when to replace the battery. Step 1002 is executed during the intermediate stage of use, and in this step 1002, the estimated remaining battery level is based on a combination of the battery energy consumption data set and voltage. Step 1003 is executed during the third stage of use, and in this step 1003, the estimated remaining battery level is determined based on voltage. If method 1000 is executed periodically (e.g., at the time of a clinician / patient visit that occurs annually or semi-annually) over the useful life of the device, different steps are executed at different times during each stage.

[0080] In one aspect, the voltage range assumes the nominal battery voltage in addition to the upper limit of the battery voltage and the lower limit of the battery voltage indicating the end of life. In the exemplary embodiments disclosed herein, for a battery having a nominal voltage of 3.1V, the upper limit of the voltage is about 3.3V and the lower limit is about 2.3V, which indicates the end of life. The following represents exemplary equations of the battery capacity estimation method described herein. These equations are exemplary, and the described threshold values are specific values related to the exemplary batteries described herein. It should be understood that these concepts can be changed as needed and applied to various other battery parameters.

[0081] At this initial stage (PH1 in FIG. 8), the estimated remaining battery capacity can be determined from the battery energy consumption data with respect to the total capacity at full charge. For example, the cumulative battery energy consumed by the IPG determined from the data can be subtracted from the total capacity of the battery. The method of using the battery energy consumption data set is also referred to as the first method or the dead reckoning method including tracking the energy consumption of various usage methods. For example, the battery energy consumption data may include calculating the energy consumption from the first energy consumption data set to the tenth energy consumption data set for various types of usage methods, as described above.

[0082] In some embodiments, the initial stage can be divided into a first sub-stage (e.g., PH1-1 in FIG. 8) and a second sub-stage (e.g., PH1-2 in FIG. 8). In the first sub-stage (e.g., PH1-1), the first method can be applied to determine the remaining battery capacity. On the other hand, in the second sub-stage (e.g., PH1-2), although the remaining battery capacity can be calculated by the first method described above, if it is overly pessimistic, a specific value, such as a lower capacity threshold, can be assigned to the estimated value. For example, applying the first method in the second sub-stage may predict a remaining battery capacity lower than the actual remaining battery capacity. Therefore, a lower limit can be applied to the remaining battery capacity during the second sub-stage.

[0083] In some embodiments, during the first sub-stage, if the capacity is greater than the upper battery capacity threshold BC upper (e.g., 70 - 50% of the full capacity, about 75%), the estimated remaining battery capacity is calculated by the first method. In some embodiments, the first sub-stage can also be determined based on a further check of the voltage against the lower battery voltage threshold V lower (e.g., 85 - 95% of the nominal battery voltage, about 92%). In some embodiments, for a battery with a nominal voltage of 3.1V, the threshold V lower is 2.87V.

[0084] Referring to FIG. 8 as an example, when the voltage is greater than the threshold V lower and the remaining battery capacity (e.g., determined by the first method) is greater than 75%, the battery can be considered to be in the first sub-stage PH1-1, and the estimated battery capacity can be considered an accurate prediction.

[0085] Referring again to FIG. 8, during the second sub-stage (e.g., PH1-2), a check can be performed to determine whether the voltage is greater than the upper battery voltage threshold V upper (e.g., 95 - 99% of the nominal voltage, about 96%, 2.974V for a battery with a nominal voltage of 3.1V). In some embodiments, a check can be performed to determine whether the estimated remaining battery capacity (e.g., determined by the first method) is less than the threshold BC upper and greater than the lower battery capacity threshold BClower. For example, the threshold BC upper can be between 70% - 80% (e.g., 75%) of the full battery capacity, and the threshold BC lower can be between 40% - 50% (e.g., 45%) of the full battery capacity. If these checks are satisfied, the remaining battery capacity determined by the first method can be considered an accurate prediction.

[0086] Referring to FIG. 8 as an example, during the second sub-stage PH1-2, when the voltage is greater than the threshold V upper (e.g., 2.974V), and / or the estimated remaining battery capacity is greater than the threshold BCupper If it is less than (for example, 75%), the value estimated by the first method can be used as the estimated remaining battery level. However, if the value estimated by the first method is less than the threshold value BC lower the estimated remaining battery level determined by the first method may be considered too pessimistic, and the lower threshold BC of the battery capacity lower is set as the lower limit, and thus the estimated value by the first method can be discarded.

[0087] During the intermediate stage of use (for example, PH2 in FIG. 8), the estimated remaining battery level can be determined based on a combination of the battery energy consumption data 920 and the voltage 930. In some embodiments, during the intermediate stage, the battery energy consumption data 920 and the voltage 930 are combined linearly, non-linearly, weighted, or in various other ways. The present disclosure is not limited to a specific combination method.

[0088] In some embodiments, during the intermediate stage, a first estimated value based on a first method using battery energy consumption data and a second estimated value based on a second method using voltage are linearly combined such that the battery energy consumption data set 920 is fully weighted at the start of the intermediate stage and the voltage is fully weighted at the end of the intermediate stage. In some embodiments, the first estimated value can be the lower limit value from the sub-stage PH1-2.

[0089] An exemplary method for estimating the remaining battery level includes: (i) determining the remaining battery level as a first function f1 of the battery voltage; and (ii) determining the remaining battery level based on the battery energy consumption data set 920 as described above. The first function f1 can be an empirical formula based on measured and / or simulated data over the battery usage period. For example, the first function f1 can be in the form of a1*V b +b1, where a1 and b1 are fitting coefficients, and V bis the battery voltage. The measured data, and / or the simulated data, includes the battery voltage and the battery capacity measured / simulated over the usage period of the IPG. In some embodiments, the measured data can be collected from an IPG implanted in a patient. In some embodiments, the simulated data can be generated by using a model related to the IPG. The simulation model of the IPG can include a physics-based model of the energy usage by the components of the IPG (including, for example, hardware and software) that mimics the operation of the IPG in use, or other models configured to simulate the operation of the IPG and the depletion of the battery.

[0090] Referring to FIG. 8 as an example, the intermediate stage PH2 corresponds to a battery voltage 930 between the upper threshold and the lower threshold (for example, between 2.974V and 2.870V). In some embodiments, if the battery charge (BC DR ) determined by the first method is less than the battery charge (BC BV ) determined based on the voltage, the estimated battery charge 940 is assigned as BC BV . Otherwise, the estimated battery charge 940 can be calculated as a linear combination of the result of applying a function f1 (for example, 1*V b +b1, where a1 = 1.4376082, b1 = -3.8259548) (for example, BC BV ) and the result of the first method using the battery energy consumption data (for example, BC DR ). For example, the linear combination function can use a second function f2 represented as a function of the voltage threshold and the battery voltage V b (i.e., 930) to combine the individual contributions from different methods. As an example, the second function f2 can be (V upper - V b ) / (V upper - V lower ). Thus, the estimated battery charge 940 in stage 2 is equal to f2*BC BV +(1 - f2)*BC DR .

[0091] In the third stage (refer to PH3 in FIG. 8), the estimated remaining battery amount 940 can be obtained based on the voltage 930. In some embodiments, the third stage occurs when the voltage 930 falls below the lower battery voltage threshold V lower . As described above, the threshold V lower can be between 85% and 95% of the battery voltage when the battery is fully charged. For example, in the case of a battery with a nominal voltage of about 3V, the threshold V lower can be between 2.8V and 2.9V.

[0092] In some embodiments, the estimated remaining battery amount 940 can be calculated using a third function f3 of the voltage V b . For example, the third function f3 can be

Equation

Equation

[0093] In some embodiments, the values of the various parameters used to determine the battery level can be provided, measured, designed, assumed, and / or calculated by the manufacturer. For example, the manufacturer's values can provide the self-discharge rate and the derated battery capacity. The measured values can be experimentally obtained, perhaps including measurements. Such experiment-based values can be used when an accurate analytical model is not feasible. When using measured values in a model, a margin can be added to account for measurement errors and future hardware and / or software changes that may increase power usage. The designed values can include values that are part of the design of the hardware or software. The assumed values can include values that are assumed (or perhaps estimated) based on experience or observation, such as how often a patient uses a patient remote control to connect to the IPG.

[0094] It is understood that the above-described embodiments, as set forth in this specification and the drawings, are to be considered in an illustrative rather than a limiting sense. However, it is obvious that various modifications and changes can be made without departing from the broad spirit and scope of the disclosure as set forth in the claims.

[0095] The terms "a", "an", and "the" and similar referents used in the context of describing the disclosed embodiments (in particular, in the context of the following claims) are to be construed to include both the singular and the plural unless otherwise specified herein or clearly contradicted by the context. The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range unless otherwise indicated herein, and each separate value is incorporated herein as if it were individually recited herein. All methods described herein may be performed in any suitable order unless otherwise specified herein or clearly contradicted by the context. It is to be understood that the terms "first", "second", "third", etc. do not necessarily limit the embodiments of the present disclosure to a particular configuration or orientation. As used herein, the term "about" means + / - 10% of the specified value. As used herein, the term "data" is understood to mean one or more values, a set of values, historical values, or cumulative values of past values. Further, disjunctive language such as the phrase "at least one of x, y, and z" is generally understood within the context in which it is used, unless otherwise specified, to mean that an item can be any one of x, y, and z, or any combination thereof (e.g., x, y, and / or z). Thus, such disjunctive language is not generally intended, and does not imply, that a particular embodiment requires each of x, y, and z to be present.

[0096] Upon reading this specification, those skilled in the art will recognize modifications of the examples herein. The inventors expect skilled artisans to adopt such modifications as appropriate, and the inventors intend for the disclosure to be practiced otherwise than as specifically described herein. Accordingly, this disclosure includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Further, any combination of any possible variations of the above-described elements is included in the present disclosure unless otherwise specified herein or clearly contradicted by the context.

[0097] (Supplementary Note) (Supplementary Note 1) An implantable pulse generator (IPG) including a battery, and an external device configured to communicate with the IPG, The IPG is used over a period including an initial stage and a later stage, The external device is configured to receive information including battery energy consumption data and the voltage of the battery from the implantable device, The external device includes a processor configured to determine an estimated value of the capacity of the battery, During the initial stage, the processor is configured to determine an estimated value of the battery capacity using the battery energy consumption data and the battery capacity before use of the IPG, During the later stage, the processor is configured to determine an estimated value of the battery capacity based on the battery voltage. A nerve stimulation system.

[0098] (Supplementary Note 2) The period of use of the IPG includes an intermediate stage between the initial stage and the later stage, and during the intermediate stage, the processor is configured to determine an estimated value of the battery capacity based on the battery energy consumption data and the battery voltage. The system according to Supplementary Note 1.

[0099] (Supplementary Note 3) The battery energy consumption data includes one or more values related to the battery energy consumed by a specific component of the IPG, or a cumulative value of the battery energy consumed by the IPG. The system according to Supplementary Note 2.

[0100] (Supplementary Note 4) During the intermediate stage, the estimated value of the battery capacity is determined from a combination of a first battery capacity estimation result based on the battery energy consumption data and a second battery estimation result based on the battery voltage. The system according to appendix 2.

[0101] (Appendix 5) The first battery capacity result and the second battery capacity result are linearly combined. The system according to appendix 4.

[0102] (Appendix 6) During the intermediate stage, the first battery capacity estimation result and the second battery capacity estimation result are linearly combined such that the first battery capacity result based on the energy consumption data is fully weighted at the start of the intermediate stage and the second battery capacity result based on the voltage is fully weighted at the end of the intermediate stage. The system according to appendix 4.

[0103] (Appendix 7) The intermediate stage occurs between an upper battery voltage threshold and a lower battery voltage threshold. The system according to appendix 4.

[0104] (Appendix 8) The upper battery voltage threshold is 95% - 99% of the nominal voltage of the battery when fully charged, and the lower battery voltage threshold is 85% - 95% of the nominal voltage of the battery. The system according to appendix 7.

[0105] (Appendix 9) When the battery has a nominal voltage of 3.1V, the upper battery voltage threshold is approximately 2.974V and the lower battery voltage threshold is 2.870V. The system according to appendix 8.

[0106] (Appendix 10) During the later stage before the voltage falls below the lower threshold of the battery voltage, the estimated value is based on the voltage. The system according to Appendix 1.

[0107] (Appendix 11) During the later stage, the estimated value is based on a polynomial function of the voltage derived from battery characteristics. The system according to Appendix 10.

[0108] (Appendix 12) The lower threshold of the battery voltage is between 85% and 95% of the nominal battery voltage of the battery. The system according to Appendix 10.

[0109] (Appendix 13) When the nominal voltage is about 3.1V, the lower threshold of the battery voltage is between 2.8V and 2.9V. The system according to Appendix 12.

[0110] (Appendix 14) During the initial stage, the estimation of the battery capacity includes subtracting the cumulative amount of energy discharged from the battery from the battery capacity at the start of use. The system according to Appendix 1.

[0111] (Appendix 15) The battery energy consumption data includes the cumulative amount of energy discharged from the battery. The system according to Appendix 14.

[0112] (Appendix 16) The cumulative amount of energy discharged from the battery is determined by the processor and stored in the IPG. The system according to Appendix 14.

[0113] (Appendix 17) The cumulative amount of energy discharged from the battery is determined by the external device and is based on the battery energy consumption data received from the IPG. The system according to Appendix 14.

[0114] (Appendix 18) The initial stage includes a first sub-stage in which the estimated value of the battery capacity exceeds the upper threshold of the battery capacity, and a second sub-stage in which the estimated value is below the upper threshold of the battery capacity. In the first sub-stage, the estimated value of the battery capacity is determined from the battery energy consumption data. In the second sub-stage, the estimated value is determined from the battery energy consumption data and has a minimum value set to the lower threshold of the battery capacity. The system according to Appendix 1.

[0115] (Appendix 19) The upper threshold of the battery capacity is approximately 75% of the battery capacity at the start of use. The system according to Appendix 18.

[0116] (Appendix 20) During the second sub-stage, the voltage is greater than the upper threshold of the battery voltage. The system according to Appendix 18.

[0117] (Appendix 21) The upper threshold of the battery voltage is between 95% and 99% of the nominal battery voltage of the battery. The system according to Appendix 20.

[0118] (Appendix 22) The upper threshold of the battery voltage is approximately 2.974V, and the battery has a nominal voltage of approximately 3.1V. The system according to Appendix 21.

[0119] (Appendix 23) During the second sub-stage, the estimated battery capacity is smaller than the upper battery capacity threshold and larger than the lower battery capacity threshold. The system according to appendix 18.

[0120] (Appendix 24) The upper battery capacity threshold is between 70% and 75% of the battery capacity at the start of use, and the second battery capacity threshold is between 40% and 50% of the battery capacity at the start of use. The system according to appendix 23.

[0121] (Appendix 25) The battery is a non-rechargeable primary battery. The system according to appendix 1.

[0122] (Appendix 26) The battery is a lithium manganese dioxide (Li-MnO2) battery. The system according to appendix 25.

[0123] (Appendix 27) The battery energy consumption data includes active battery energy consumption data related to the delivery of stimulation pulses and fixed battery energy usage data related to fixed energy consumption. The system according to appendix 1.

[0124] (Appendix 28) The fixed battery energy usage data is fixed battery discharge, and periodic communication with the external device, and one or more housekeeping tasks related to the functions of the software within the IPG, and the quiescent current consumed by the IPG, and includes battery energy usage related to at least one or any combination of The system according to appendix 27.

[0125] (Appendix 29) The fixed battery energy usage dataset includes a first usage dataset related to the energy used by the self-discharge of the battery, a second usage dataset related to the energy used by communication polling at a first scan rate for determining whether the external device is requesting communication, a third usage dataset related to the energy used by radio frequency communication including communication with the external device, a fourth usage dataset related to the energy used by communication at a second scan rate for determining whether the external device is requesting communication, a fifth usage dataset related to the energy used by one or more housekeeping tasks, a sixth usage dataset related to the energy used by the quiescent current consumed by the IPG, a seventh usage dataset related to the energy used by the inventory discharge experienced by the battery before connecting to the IPG, an eighth usage dataset related to the energy used by communication with the external device during the embedding of the IPG, including at least one or any combination of the system described in Appendix 28.

[0126] (Appendix 30) The active usage includes a ninth usage dataset related to the energy usage for delivering stimulation pulses by the processor and a tenth usage dataset related to the energy used by the stimulation generation circuit configuration of the IPG for delivering stimulation pulses, the system described in Appendix 27.

[0127] (Appendix 31) An external device that can be communicably connected to an implantable device disposed inside a patient, the implantable device including a battery, the external device A graphical user interface configured to facilitate programming and monitoring of the implantable device, A processor connected to a memory device, Comprising, The processor Establishes communication with the implantable device, Receives information including battery energy consumption data and the voltage of the battery from the implantable device, Based on the received information, determines an estimated value of the capacity of the battery. Is configured to, The processor is configured to determine an estimated value of the capacity of the battery during an initial stage of use of the battery using cumulative battery energy consumption data with respect to the total capacity of the battery at the start of use of the battery. The processor is configured to determine an estimated value of the battery capacity during an intermediate stage of use based on a combination of the cumulative battery energy consumption data set and the voltage of the battery. The processor is configured to determine an estimated value of the capacity of the battery during a third stage of use based on the voltage of the battery. External device.

[0128] (Appendix 32) The cumulative battery energy consumption is battery energy consumption data received from the implantable device. The external device according to Appendix 31.

[0129] (Appendix 33) The cumulative battery energy consumption is determined by the external device from the cumulative battery energy data received from the implantable device, and the cumulative battery energy data includes a data set of one or more values. The external device according to Appendix 31.

[0130] (Appendix 34) During the intermediate stage, the estimated value of the battery capacity is determined from a combination of a first battery capacity estimation result based on the battery energy consumption data and a second battery estimation result based on the voltage. The external device according to Appendix 31.

[0131] (Appendix 35) The first battery capacity result and the second battery capacity result are linearly combined. The external device according to Appendix 34.

[0132] (Appendix 36) During the intermediate stage, the first battery capacity estimation result and the second battery capacity estimation result are linearly combined such that the first battery capacity result based on the energy consumption data is fully weighted at the start of the intermediate stage, and the second battery capacity result based on the voltage is fully weighted at the end of the intermediate stage. The external device according to Appendix 34.

[0133] (Appendix 37) The intermediate stage occurs between an upper battery voltage threshold and a lower battery voltage threshold. The external device according to Appendix 34.

[0134] (Appendix 38) When the battery has a nominal voltage of 3.1V, the upper battery voltage threshold is approximately 2.974V and the lower battery voltage threshold is 2.870V. The external device according to Appendix 37.

[0135] (Appendix 39) During the third stage, when the voltage is below the lower threshold of the battery voltage, the estimated value of the battery capacity is based on the battery voltage. The external device described in Supplementary Note 31.

[0136] (Supplementary Note 40) During the third stage, the estimated value of the battery capacity is based on the polynomial function of the battery voltage derived from the battery characteristics. The external device described in Supplementary Note 39.

[0137] (Supplementary Note 41) The lower threshold of the battery voltage is between 85% and 95% of the nominal battery voltage of the battery. When the nominal voltage of the battery is about 3.1V, the lower threshold of the battery voltage is between 2.8V and 2.9V. The external device described in Supplementary Note 39.

[0138] (Supplementary Note 42) During the initial stage, the estimation of the battery capacity includes subtracting the cumulative amount of energy discharged from the battery from the battery capacity at the start of use of the battery. The external device described in Supplementary Note 31.

[0139] (Supplementary Note 43) The initial stage includes a first sub-stage in which the estimated value exceeds the upper threshold of the battery capacity and a second sub-stage in which the estimated value is below the upper threshold of the battery capacity. In the first sub-stage, the estimated value of the battery capacity is determined from the battery energy consumption data with respect to the full capacity. In the second sub-stage, the estimated value of the battery capacity is determined from the battery energy consumption data with respect to the full capacity and has a minimum value set as the lower threshold of the battery capacity. The external device described in Supplementary Note 31.

[0140] (Supplementary Note 44) The upper battery capacity threshold is about 75% of the battery capacity at the start of use, and the lower battery capacity is about 46% of the battery capacity at the start of use. The external device described in Supplementary Note 43.

[0141] (Supplementary Note 45) During the second sub-stage, the voltage is greater than the upper battery voltage threshold. The external device described in Supplementary Note 43.

[0142] (Supplementary Note 46) The upper battery voltage threshold is between 95% and 99% of the nominal battery voltage of the battery. When the nominal voltage of the battery is about 3.1V, the upper battery voltage is about 2.974V. The external device described in Supplementary Note 45.

[0143] (Supplementary Note 47) The battery energy consumption data An active battery energy consumption data set related to the delivery of stimulation pulses, A fixed battery energy usage data set related to the fixed energy consumption, and The external device described in Supplementary Note 31.

[0144] (Supplementary Note 48) The fixed battery energy usage data set includes fixed battery discharge, periodic communication with the external device, housekeeping tasks related to the functions of the software in the implantable device, the quiescent current consumed by the implantable device, and includes battery energy usage related to at least one or any combination of The external device described in Supplementary Note 47.

[0145] (Supplementary Note 49) A method for determining an estimated value of the battery capacity of a battery of an implantable device over a period of use, establishing communication with the implantable device by an external device; receiving information including battery energy consumption data and the voltage of the battery from the implantable device; determining an estimated value of the battery capacity based on the received information; comprising during an initial stage of the period of use, the estimated value of the battery capacity is determined from the battery energy consumption data with respect to the battery capacity at the start of the period of use, during a later stage of the period of use, the estimated value of the battery capacity is based on the battery voltage, method.

[0146] (Appendix 50) The later stage is the final stage of the period of use. The method according to Appendix 49.

[0147] (Appendix 51) The period of use includes an intermediate stage between the initial stage and the later stage, and during the intermediate stage, the estimated value of the battery capacity is determined based on the battery energy consumption data and the battery voltage. The method according to Appendix 49.

Claims

1. An implantable pulse generator (IPG) including a battery, and an external device configured to communicate with the IPG, wherein the IPG is used over a period including an initial stage and a later stage, the external device is configured to receive from the implantable device information including battery energy consumption data and the voltage of the battery, the external device includes a processor configured to determine an estimated value of the capacity of the battery, during the initial stage, the processor is configured to determine an estimated value of the battery capacity using the battery energy consumption data and the battery capacity before use of the IPG, during the later stage, the processor is configured to determine the estimated value of the battery capacity based on the battery voltage, A nerve stimulation system.

2. The period of use of the IPG includes an intermediate stage between the initial stage and the later stage, and during the intermediate stage, the processor is configured to determine an estimated value of the battery capacity based on the battery energy consumption data and the battery voltage, The system according to claim 1.

3. The battery energy consumption data includes one or more values related to the battery energy consumed by a specific component of the IPG, or a cumulative value of the battery energy consumed by the IPG, The system according to claim 2.

4. During the intermediate stage, the estimated value of the battery capacity is determined from a combination of a first battery capacity estimation result based on the battery energy consumption data and a second battery estimation result based on the battery voltage, The system according to claim 2.

5. The first battery capacity result and the second battery capacity result are linearly combined, The system according to claim 4.

6. During the intermediate stage, the first battery capacity estimation result and the second battery capacity estimation result are linearly combined such that the first battery capacity result based on the energy consumption data is fully weighted at the start of the intermediate stage and the second battery capacity result based on the voltage is fully weighted at the end of the intermediate stage, The system according to claim 4.

7. The intermediate stage occurs between an upper battery voltage threshold and a lower battery voltage threshold, The system according to claim 4.

8. The upper battery voltage threshold is 95% to 99% of the nominal voltage of the battery at full charge, and the lower battery voltage threshold is 85% to 95% of the nominal voltage of the battery. The system according to claim 7.

9. When the battery has a nominal voltage of 3.1 V, the upper battery voltage threshold is about 2.974 V, and the lower battery voltage threshold is 2.870 V. The system according to claim 8.

10. During the later stage when the voltage is below the lower battery voltage threshold, the estimated value is based on the voltage. The system according to claim 1.

11. During the later stage, the estimated value is based on a polynomial function of the voltage derived from battery characteristics. The system according to claim 10.

12. The lower battery voltage threshold is between 85% and 95% of the nominal battery voltage of the battery. The system according to claim 10.

13. When the nominal voltage is about 3.1 V, the lower battery voltage threshold is between 2.8 and 2.9 V. The system according to claim 12.

14. During the initial stage, the estimation of the battery capacity includes subtracting the cumulative amount of energy discharged from the battery from the battery capacity at the start of use. The system according to claim 1.

15. The battery energy consumption data includes the cumulative amount of energy discharged from the battery. The system according to claim 14.

16. The cumulative amount of energy discharged from the battery is determined by the processor and stored in the IPG. The system according to claim 14.

17. The cumulative amount of energy discharged from the battery is determined by the external device and is based on the battery energy consumption data received from the IPG. The system according to claim 14.

18. The initial stage includes a first sub-stage where the estimated value of the battery capacity exceeds the upper battery capacity threshold, and a second sub-stage where the estimated value is below the upper battery capacity threshold. In the first sub-stage, the estimated value of the battery capacity is determined from the battery energy consumption data. In the second sub-stage, the estimated value is determined from the battery energy consumption data and has a minimum value set to the lower battery capacity threshold. The system according to claim 1.

19. The upper battery capacity threshold is approximately 75% of the battery capacity at the start of use. The system according to claim 18.

20. During the second sub-phase, the voltage is greater than the upper battery voltage threshold. The system according to claim 18.

21. The upper battery voltage threshold is between 95% and 99% of the nominal battery voltage of the battery. The system according to claim 20.

22. The upper battery voltage threshold is approximately 2.974 V, and the battery has a nominal voltage of approximately 3.1 V. The system according to claim 21.

23. During the second sub-phase, the estimated battery capacity is less than the upper battery capacity threshold and greater than the lower battery capacity threshold. The system according to claim 18.

24. The upper battery capacity threshold is between 70% and 75% of the battery capacity at the start of use, and the second battery capacity threshold is between 40% and 50% of the battery capacity at the start of use. The system according to claim 23.

25. The battery is a non-rechargeable primary battery. The system according to claim 1.

26. The battery is a lithium manganese dioxide (Li-MnO ２ ) battery. The system according to claim 25.

27. The battery energy consumption data includes active battery energy consumption data related to the delivery of stimulation pulses and fixed battery energy usage data related to fixed energy consumption. The system according to claim 1.

28. The fixed battery energy usage data includes fixed battery discharge, periodic communication with the external device, one or more housekeeping tasks related to the functions of the software within the IPG, the quiescent current consumed by the IPG, and includes battery energy usage related to at least one or any combination of the above. The system according to claim 27.

29. The fixed battery energy usage data set includes a first usage data set related to the energy used by self-discharge of the battery, a second usage data set related to the energy used by communication polling at a first scan rate for determining whether the external device is requesting communication, a third usage data set related to the energy used by wireless frequency communication including communication with the external device. A fourth usage data set related to the energy used by communication at a second scan rate for determining whether the external device is requesting communication; A fifth usage data set related to the energy used by one or more housekeeping tasks; A sixth usage data set related to the energy used by the quiescent current consumed by the IPG; A seventh usage data set related to the energy used by the self-discharge during inventory that the battery experiences before connecting to the IPG; An eighth usage data set related to the energy used by communication with the external device during the embedding of the IPG; including at least one or any combination of: The system according to claim 28.

30. The active usage includes a ninth usage data set related to the energy usage for delivering stimulation pulses by the processor and a tenth usage data set related to the energy used by the stimulation generation circuit configuration of the IPG for delivering the stimulation pulses. The system according to claim 27.

31. An external device communicably connectable to an implantable device disposed inside a patient, the implantable device including a battery, and the external device has a graphical user interface configured to facilitate programming and monitoring of the implantable device; a processor connected to a memory device; and is configured to: The processor establishes communication with the implantable device; receives information including battery energy consumption data and the voltage of the battery from the implantable device; determines an estimated value of the capacity of the battery based on the received information; and is configured to: The processor is configured to determine an estimated value of the capacity of the battery during an initial stage of use of the battery using cumulative battery energy consumption data with respect to the total capacity of the battery at the start of use of the battery; The processor is configured to determine an estimated value of the battery capacity during an intermediate stage of use based on a combination of the cumulative battery energy consumption data set and the voltage of the battery. The processor is configured to determine an estimated value of the capacity of the battery during a third stage of use based on the voltage of the battery. External device. **Claim 32** The cumulative battery energy consumption is battery energy consumption data received from the implantable device. The external device according to claim 31. **Claim 33** The cumulative battery energy consumption is determined by the external device from the cumulative battery energy data received from the implantable device, and the cumulative battery energy data includes a data set of one or more values. The external device according to claim 31. **Claim 34** During the intermediate stage, the estimated value of the battery capacity is determined from a combination of a first battery capacity estimation result based on the battery energy consumption data and a second battery estimation result based on the voltage. The external device according to claim 31. **Claim 35** The first battery capacity result and the second battery capacity result are linearly combined. The external device according to claim 34. **Claim 36** During the intermediate stage, the first battery capacity estimation result and the second battery capacity estimation result are linearly combined such that the first battery capacity result based on the energy consumption data is fully weighted at the start of the intermediate stage and the second battery capacity result based on the voltage is fully weighted at the end of the intermediate stage. The external device according to claim 34. **Claim 37** The intermediate stage occurs between an upper battery voltage threshold and a lower battery voltage threshold. The external device according to claim 34. **Claim 38** When the battery has a nominal voltage of 3.1 V, the upper battery voltage threshold is about 2.974 V and the lower battery voltage threshold is 2.870 V. The external device according to claim 37. **Claim 39** During the third stage, when the voltage is below the lower battery voltage threshold, the estimated value of the battery capacity is based on the battery voltage. The external device according to claim 31. **Claim 40** During the third stage, the estimated value of the battery capacity is based on a polynomial function of the battery voltage derived from battery characteristics. The external device according to claim 39. **Claim 41** The lower battery voltage threshold is between 85% and 95% of the nominal battery voltage of the battery. When the nominal voltage of the battery is about 3.1 V, the lower battery voltage threshold is between 2.8 V and 2.9 V. The external device according to claim 39.

42. During the initial stage, the estimation of the battery capacity includes subtracting the cumulative amount of energy discharged from the battery from the battery capacity at the start of use of the battery. The external device according to claim 31.

43. The initial stage includes a first sub-stage in which the estimated value exceeds the upper battery capacity threshold and a second sub-stage in which the estimated value is below the upper battery capacity threshold. In the first sub-stage, the estimated value of the battery capacity is determined from the battery energy consumption data with respect to the full capacity. In the second sub-stage, the estimated value of the battery capacity is determined from the battery energy consumption data with respect to the full capacity and has a minimum value set to the lower battery capacity threshold. The external device according to claim 31.

44. The upper battery capacity threshold is about 75% of the battery capacity at the start of use, and the lower battery capacity is about 46% of the battery capacity at the start of use. The external device according to claim 43.

45. During the second sub-stage, the voltage is greater than the upper battery voltage threshold. The external device according to claim 43.

46. The upper battery voltage threshold is between 95% and 99% of the nominal battery voltage of the battery. When the nominal voltage of the battery is about 3.1 V, the upper battery voltage is about 2.974 V. The external device according to claim 45.

47. The battery energy consumption data An active battery energy consumption data set related to the delivery of stimulation pulses and A fixed battery energy usage data set related to fixed energy consumption including The external device according to claim 31.

48. The fixed battery energy usage data set Fixed battery discharge, Regular communication with the external device, Housekeeping tasks related to the functions of the software in the implantable device, The quiescent current consumed by the implantable device including the battery energy usage related to at least one or any combination of The external device according to claim 47.

49. A method for determining an estimated value of the battery capacity of a battery of an implantable device over a period of use, comprising: establishing communication with the implantable device by an external device; receiving from the implantable device information including battery energy consumption data and the voltage of the battery; determining an estimated value of the battery capacity based on the received information; wherein during an initial stage of the period of use, the estimated value of the battery capacity is determined from the battery energy consumption data for the battery capacity at the start of the period of use; during a later stage of the period of use, the estimated value of the battery capacity is based on the battery voltage. A method.

50. The later stage is the final stage of the period of use. The method according to claim 49.

51. The period of use includes an intermediate stage between the initial stage and the later stage, and during the intermediate stage, the estimated value of the battery capacity is determined based on the battery energy consumption data and the battery voltage. The method according to claim 49.

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