System for determining battery capacity estimate for implantable device

Abstract

A system for determining an estimate of battery capacity of an implantable device battery used over a period of time. The system includes an external device having a processor configured to communicate with the implantable device. In the initial phase, the processor is configured to determine an estimate of the battery capacity using the battery energy consumption data and the battery capacity at the beginning of use. In a later stage, the processor is configured to determine an estimate of the battery capacity based on the battery voltage. The system may include an intermediate phase, and in the intermediate phase, the processor is configured to determine an estimate of the battery capacity based on the battery energy consumption data and the battery voltage.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to U.S. Provisional Patent Application Serial No. 63 / 359,463 filed on July 8, 2022, the entire contents of which are incorporated by reference into this application. Background Art

[0003] The present disclosure relates generally to implantable medical devices, such as neural stimulation therapy systems, and more particularly to methods and systems for determining an estimate of the remaining capacity of a battery of an implanted medical device. The remaining capacity is the amount of energy in the battery available for use at a given point in time.

[0004] In recent years, treatment using implantable medical devices such as neurostimulation systems has become increasingly common. For example, stimulation systems typically utilize an array of electrodes to treat one or more target neural structures. The electrodes are typically mounted together on a multi-electrode lead that is implanted into the patient's tissue in a location designed to electrically couple the electrodes to the target neural structures, typically with at least some coupling provided via intervening tissue. Other approaches may also be employed, such as attaching one or more electrodes to the skin overlying the target neural structure, implanting them in a cuff around the target nerve, and so forth. Regardless, a physician typically establishes an appropriate treatment regimen by varying the electrical stimulation applied to the electrodes.

[0005] The neural tissue structure may vary significantly from patient to patient. The electrical properties of the tissue structure surrounding the target neural structure may also vary significantly from patient to patient, and the neural response to stimulation may vary significantly, and the electrical stimulation pulse pattern, pulse width, frequency and / or amplitude may effectively affect physical function in one patient and may cause significant discomfort or pain, or have limited effect, in another patient. Even in patients where an implanted neurostimulation system provides effective treatment, frequent adjustments and changes to the stimulation regimen are often required before an appropriate treatment regimen is determined, which often requires repeated visits and considerable discomfort to the patient before therapeutic effects are achieved. Such implanted neurostimulation devices typically include a battery that meets the power requirements of the device when performing stimulation as well as control and telemetry functions.

[0006] As a result, the useful life and battery life of such devices is limited and can vary significantly depending on usage. Previously, implantable systems used non-rechargeable batteries that were typically replaced every five to seven years. More recently, implantable systems use batteries that need to be replaced every ten years or more. Because replacement requires additional surgery, patient discomfort, and significant costs to the healthcare system, it is imperative that both patients and clinicians accurately estimate the battery capacity periodically over the useful life of the battery. Currently, there is often uncertainty about the remaining life of a battery, particularly because the parameters of a battery may remain roughly consistent over most of the battery's useful life and rapidly degrade at the end of its useful life. Current systems and methods for determining battery capacity generally do not provide a consistent and accurate assessment of the remaining battery life. Therefore, it would be desirable to provide more accurate and reliable predictions and / or estimates of the remaining capacity of batteries contained in implantable devices, particularly for neurostimulation systems, which could greatly improve patient comfort and address uncertainty in device replacement scheduling and timing. Summary of the invention

[0007] Aspects of the present disclosure relate to methods, systems and devices for determining the remaining battery capacity of a battery of an implantable device, such as an implantable pulse generator (IPG). Typically, the battery is a non-rechargeable primary cell, such as a lithium manganese dioxide (Li-MnO2) battery.

[0008] One aspect of the present disclosure relates to a method for determining an estimated remaining battery capacity of a battery in an implantable device over the life of the device. Such a method may include establishing communication between an implantable device and an external device (e.g., a clinician programmer, a patient remote control, or other device), and receiving information from the implantable device, the information including battery energy consumption data (e.g., a data set of energy consumed by a specific load on the battery or a cumulative value of energy consumed by the load on the battery) and battery voltage. Based on the received information, an estimated remaining battery capacity is determined. In some embodiments, a method for determining an estimated remaining battery capacity is used that can employ one or more techniques or equations. In some embodiments, the technique or equation depends on the stage of the time period of the battery's life (e.g., an initial stage, an intermediate stage, a third stage, a later stage).

[0009] In some embodiments, during an initial phase of use, the estimated remaining battery capacity is determined based on data relating to cumulative battery energy consumption relative to the total capacity of the battery when fully charged. During an intermediate phase of use, the estimated remaining battery capacity is based on a combination of battery energy consumption data and battery voltage when determining the estimated battery capacity. During a tertiary phase of use, the estimated remaining battery capacity is determined based on voltage. In some embodiments, the method includes estimating the remaining battery capacity based on cumulative battery energy consumption during the initial phase, and estimating the battery capacity based on voltage during a later phase. In some embodiments, the later phase is immediately after the initial phase, while in other embodiments, the later phase is the third or final phase.

[0010] In some embodiments, during the intermediate stage, the first estimate based on the battery energy consumption data and the second estimate based on the voltage are linearly combined. In some embodiments, the intermediate stage is when the voltage is within a voltage range between a battery voltage upper threshold (e.g., 95%-99% of the open circuit nominal voltage) and a battery voltage lower threshold (e.g., 85%-95% of the nominal voltage). The nominal voltage can be provided by the battery manufacturer (e.g., 3.1V for the Litronik LiS battery described below) 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 the first battery estimate based on the energy consumption data is fully weighted when the intermediate stage starts and the second estimate based on the voltage is fully weighted when the intermediate stage is completed.

[0011] In some embodiments, the third stage occurs when the voltage is below a lower voltage threshold (eg, between 85-95% of the nominal voltage). In some embodiments, the estimate during the third stage is determined from the voltage by a polynomial equation derived based on characterization data of the battery discharge.

[0012] In some embodiments, the initial stage may include a first sub-stage and a second sub-stage. During the first sub-stage, when the estimated capacity is greater than an upper capacity threshold (e.g., between 70%-80% of the capacity, approximately 75%), the estimated remaining battery capacity is determined based on the battery consumption data. During the second sub-stage, when the estimated capacity is lower than the upper capacity threshold, the estimate is determined based on the battery consumption and is bottomed (i.e., has a minimum value) at the lower capacity threshold (e.g., the capacity is between 40%-50%, approximately 46%). In addition, during the second sub-stage, the voltage may be greater than the battery voltage upper threshold. During the initial stage, the remaining battery capacity may be determined based on a battery consumption data set (e.g., dead reckoning technology).

[0013] Another aspect relates to an external device (e.g., a clinician programmer, a patient remote control, or other device) configured to determine the remaining battery capacity according to the above method. In some embodiments, the external device is communicatively coupled 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 coupled to a memory having executable instructions recorded thereon to perform the method described herein. For example, the instructions may be configured to establish communication between the implantable device and the programmer, and receive information from the implantable device, the information including battery energy consumption data and a battery voltage of the implantable device, and determine an estimated remaining battery capacity based on the received information. In an initial stage of use, the estimated remaining battery capacity is determined based on the cumulative battery energy consumption relative to the total capacity when fully charged. In an intermediate stage of use, the estimated remaining battery capacity is determined based on a combination of the battery energy consumption data and the voltage. In a third stage of use, the estimated remaining battery capacity is determined based on the voltage. In some embodiments, the external device is configured to estimate the remaining battery capacity based on the cumulative battery energy consumption in the initial stage, and to estimate the battery capacity based on the voltage in a later stage. In some embodiments, the later stage immediately follows the initial stage, while in other embodiments, the later stage is the third or final stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 An exemplary neurostimulation system is schematically shown including a clinician programmer and a patient remote control for positioning and / or programming a trial neurostimulation system and a permanently implanted neurostimulation system.

[0015] Figure 2A-2C Diagram showing the neural structures along the spine, lower back, and sacral region.

[0016] Figure 3 An example of a fully implanted neurostimulation system is shown.

[0017] Figure 4 An example of a neurostimulation system with an implantable stimulation lead, an implantable pulse generator, and an external charging device is shown.

[0018] Figure 5A and Figure 5B Shown is a detailed view of an implantable pulse generator (IPG) and related components for a neurostimulation system.

[0019] Figure 6 A schematic diagram of one embodiment of an IPG architecture is shown.

[0020] Figure 7An example battery discharge curve is shown that plots battery voltage as a function of remaining battery capacity, including different stages for determining remaining battery capacity, according to some embodiments.

[0021] Figure 8 An example of a battery discharge curve is shown in accordance with some embodiments, where the axes are reversed to show remaining capacity (percent of full capacity) as a function of voltage, and also showing different stages and sub-stages for determining remaining battery capacity.

[0022] Fig. 9 and 10 An example flow chart of a method for estimating remaining battery capacity of an implantable device according to some embodiments is shown. DETAILED DESCRIPTION

[0023] The present application relates to neurostimulation therapy systems and related implantable devices and programmer devices, and in particular, methods and systems for determining an estimate of the remaining battery capacity of a battery of an implantable device. In the example embodiments described herein, the system is a sacral neurostimulation therapy system that is configured to treat overactive bladder ("OAB") and relieve symptoms of bladder-related dysfunction. However, it should be recognized that the devices, systems, and methods disclosed herein can also be used for a variety of neuromodulation purposes, such as bowel dysfunction, and the treatment of pain or other indications, such as movement or affective disorders, or for any implantable medical device powered by a battery.

[0024] Neurostimulation (or neuromodulation, used interchangeably herein) therapy systems (such as any of the systems described herein) can be used to treat a variety of diseases and associated symptoms, such as acute pain disorders, movement disorders, affective disorders, and bladder-related dysfunction. Examples of pain disorders that can be treated by neurostimulation include failed lumbar surgery syndrome, reflex sympathetic dystrophy or complex regional pain syndrome, causalgia, arachnoiditis, and peripheral neuropathy.

[0025] Movement disorders include muscle paralysis, tremor, dystonia, and Parkinson's disease. Affective disorders include depression, obsessive-compulsive disorder, cluster headaches, Tourette syndrome, and certain types of chronic pain. Bladder-related dysfunction includes, but is not limited to, OAB, urge incontinence, urinary frequency, and urinary retention. OAB is one of the most common urinary dysfunctions 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.

[0026] Neurostimulation approaches include sacral neuromodulation (SNM). SNM is a well-established treatment approach that offers a safe, effective, reversible, and durable treatment option for OAB management. SNM therapy involves stimulating the sacral nerves located in the lower back using gentle electrical pulses. Electrodes are placed next to the sacral nerves, typically at the S3 level, by inserting leads into the corresponding sacral foramina. The leads are inserted subcutaneously and subsequently attached to an implantable pulse generator (IPG), also referred to herein as an implantable neurostimulator or neurostimulator.

[0027] Figure 1 An exemplary neurostimulation system is schematically shown, which includes a trial neurostimulation system 200 and a permanently implanted neurostimulation system 100. Both the external pulse generator (EPG) 80 and the implantable pulse generator (IPG) 10 are compatible with and wirelessly communicate with a clinician programmer 60 and a patient remote control 70, which are used to position and / or program the trial neurostimulation system 200 and / or the permanently implanted system 100 after a successful trial. The clinician programmer can include dedicated software, dedicated hardware, or both to assist in lead placement, programming, reprogramming, stimulation control and / or parameter settings, and determining remaining battery capacity estimates. In addition, the patient remote control 70 can be used with one or both of the IPG and the EPG to provide at least some control of the stimulation (e.g., start a preset program, increase or decrease stimulation) and / or monitor battery status.

[0028] In one aspect, the physician uses a clinician programmer 60 to adjust the settings of the EPG and / or IPG while the leads are implanted in the patient. The clinician programmer can be a tablet computer used by the clinician to program the IPG, or to control the EPG during a trial. The patient remote control 70 allows the patient to turn stimulation on or off, or to change the stimulation from the IPG when implanted, or to change the stimulation from the EPG during a trial phase.

[0029] In another aspect, a clinician programmer 60 has a control unit that may include a microprocessor and dedicated computer code instructions for implementing the methods and systems used by a physician in deploying the therapy system, setting therapy parameters, and periodically evaluating the IPG, including the remaining battery capacity estimation described herein. The clinician programmer typically includes a user interface, which may be a graphical user interface.

[0030] The electrical pulses generated by the EPG and IPG are delivered to one or more target nerves through one or more electrodes located at or near the distal end of one or more leads. The size, shape, and material of the leads may vary and may be customized for a specific therapeutic application. For the SNM system, the leads are of a suitable size and length to extend from the IPG and through one of the sacral foramina to the target sacral nerve. However, the leads and / or stimulation programs may vary depending on the target nerve.

[0031] Figure 2A FIG. 2C shows a diagram of various neural structures in a patient that may be targeted during neurostimulation therapy. Figure 2A The different segments of the spinal cord and the corresponding nerves within each segment are shown. The spinal cord is a long, thin bundle of nerves and supporting cells that extends from the brain stem along the cervical spinal cord, through the thoracic spinal cord, and into the space between the first and second lumbar vertebrae in the lumbar cord. After leaving the spinal cord, nerve fibers split into multiple branches that innervate various muscles and organs, carrying sensory and control impulses between the brain and the organs and muscles. Because some nerves may have branches that innervate certain organs and branches that innervate certain muscles, stimulating a nerve near a nerve root near the spinal cord or a nerve root near the spinal cord can stimulate the branch that innervates the target organ or muscle.

[0032] Figure 2B Nerves associated with the lower back region are shown, where in the lower lumbar region, the nerve bundles exit the spinal cord and pass through the sacral foramen of the sacrum. In some embodiments, the lead is advanced through the sacral foramen until the electrode is located at the anterior sacral nerve root, and the anchor portion of the lead is typically located dorsal to the sacral foramen through which the lead is passed to secure the lead. Figure 2C A detailed view of the nerves of the lumbosacral trunk and sacral plexus is shown, particularly the S1-S5 nerves of the lower sacrum. The S3 sacral nerve is particularly useful for treating bladder-related dysfunction, particularly OAB.

[0033] Figure 3 An example of a fully implantable neurostimulation system 100 suitable for sacral nerve stimulation is schematically shown. The neurostimulation system 100 includes an IPG 10 implanted in the lower back region and connected to a lead 20 extending through the S3 foramen for stimulating the S3 sacral nerve. The lead is anchored by a toothed anchoring portion 30 that maintains the position of a set of neurostimulation electrodes 40 along a target nerve, which in this example is the anterior sacral nerve root S3, which energizes the bladder to provide treatment for various bladder-related dysfunctions. While this embodiment is suitable for sacral nerve stimulation, it will be appreciated that a similar system may be used to stimulate a target peripheral nerve or the posterior epidural space of the spine to treat other indications.

[0034] exist Figure 3In the illustrated embodiment, the implantable neurostimulation system 100 includes a controller in an IPG having one or more pulse programs, schedules, or patterns that can be pre-programmed or created as described above. In some embodiments, these same attributes associated with the IPG can be used for an EPG of a partially implanted trial system used prior to implanting a permanent neurostimulation system 100.

[0035] Figure 4 An example neurostimulation system 100 is shown that is fully implantable and suitable for sacral nerve stimulation treatment. The implantable system 100 includes an IPG 10 coupled to a neurostimulation lead 20 that includes a set of neurostimulation electrodes 40 located at the distal end of the lead. The lead includes a lead anchoring portion 30 having a series of radially outwardly extending prongs for anchoring the lead and maintaining the position of the lead 20 after implantation. The lead 20 may also include one or more radiopaque markers 25 to assist in locating the lead using visualization techniques (e.g., fluoroscopy). In some embodiments, the IPG provides monopolar or bipolar electrical pulses that are delivered to the target nerve via one or more electrodes (typically four electrodes). In sacral nerve stimulation, the lead is typically implanted through the S3 foramen, as described herein.

[0036] The system may also include a patient remote control 70 and a clinician programmer 60, both configured to communicate wirelessly with an implanted IPG during long-term treatment or with an EPG during a trial. The clinician programmer 60 may be a tablet computer used by the clinician to program the IPG and the EPG. The patient remote control may be a battery-operated portable device that communicates with the EPG and the IPG using radio frequency (RF) signals and allows the patient to adjust the stimulation level, check the status of the IPG battery charge, and / or turn stimulation on or off. The IPG may use RF polling at different scan rates to facilitate a communication session with the clinician programmer and the patient remote control.

[0037] Figure 5A and Figure 5BA detailed view of the IPG 10 and its internal components is shown. In some embodiments, the pulse generator can generate one or more non-ablative electrical pulses that are delivered to the nerves to produce a desired effect, such as controlling pain or inhibiting, preventing, or disrupting nerve activity to treat OAB or bladder-related dysfunction. In some applications, pulses having pulse amplitude ranges between 0 mA to 1,000 mA, 0 mA to 100 mA, 0 mA to 50 mA, 0 mA to 25 mA, and / or any other or intermediate amplitude ranges can be used. The pulse generator may include a controller (e.g., a processor) and / or a memory that is suitable for providing instructions to and receiving information from other components of the implantable neurostimulation system. The processor may include a microprocessor, for example, from or Advanced Micro Devices, etc. commercially available microprocessors.

[0038] One or more properties of the electrical pulses may be controlled by a processor or controller of the IPG or EPG. These properties may include, for example, the frequency, intensity, pattern, duration or other aspects of the timing and amplitude of the electrical pulses. In addition, the controller may change the voltage and current used to generate the pulses. The controller may establish a repeatable pulse pattern or program applied to the electrodes. For example, the system may select 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, having one or more pulse programs, plans or patterns that can be created and / or preprogrammed. In some embodiments, the IPG may be programmed to change stimulation parameters, including pulse amplitudes in the range of 0mA to 10mA, pulse widths in the range of 50μs to 500μs, pulse frequencies in the range of 5Hz to 250Hz, stimulation modes (e.g., continuous or cyclic) and electrode configurations (e.g., anode, cathode or off) to achieve the best treatment results for the patient. Even though each parameter may vary from person to person, this programming allows the best settings to be determined for each patient.

[0039] like Figure 5A and 5BAs shown, the IPG 10 can include a head portion 11 at one end. The head portion 11 accommodates a feedthrough assembly 12, a connector stack 13, and a communication antenna 16 to facilitate wireless communication with a clinician programmer and / or a patient remote control. The IPG 10 includes a housing 17 that accommodates a circuit 23, which includes a printed circuit board, a memory, and a controller assembly for implementing the above-mentioned electrical pulse program. A battery 24 is also contained in the housing 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., a Litronik LiS 3150MK battery with a capacity of 1200mAh and a nominal voltage of 3.1V). However, it will be appreciated that any suitable battery may be used as required for a particular application.

[0040] Figure 6 Schematic diagram of one embodiment of the architecture of IPG 10 is shown. In some embodiments, each component of the architecture of IPG 10 may be implemented using a processor, memory, and / or other hardware components of IPG 10. In some embodiments, the components of the architecture of IPG 10 may include software that interacts with the hardware of IPG 10 to achieve desired results, and the components of the architecture of IPG 10 may be located within a housing.

[0041] In some embodiments, IPG 10 may include, for example, a communication module 600. Communication module 600 may be configured to send and receive data to and from other components and / or devices of an exemplary neurostimulation system, including, for example, clinician programmer 60 and / or patient remote control 70. In some embodiments, communication module 600 may include one or more antennas and software configured to control the one or more antennas to send and receive information to and from one or more of the other components of IPG 10.

[0042] The IPG 10 may also include a data module 602. The data module 602 may be configured to manage data related to the identity and attributes of the IPG 10. In some embodiments, the data module 602 may include one or more databases, which may include, for example, information related to the IPG 10 stored on a storage device. The information may include, for example, an identification of the IPG 10 or one or more attributes of the IPG 10. In one embodiment, the information related to the attributes of the IPG 10 may include, for example, data identifying the functionality of the IPG 10, historical stimulation program data, power consumption of the IPG 10, and battery consumption data, which may include one or more types of battery usage data, cumulative battery consumption values, data identifying the charging capacity of the IPG 10 and / or the power storage capacity of the IPG 10, and various monitoring parameters, including battery voltage measurements.

[0043] The IPG 10 may include a pulse control 604. In some embodiments, the pulse control 604 may be configured to control the IPG 10 to generate one or more pulses. In some embodiments, for example, this may be performed based on information identifying one or more pulse patterns, programs, and the like. The information may further specify, for example, the frequency of the pulses generated by the IPG 10, the duration of the pulses generated by the IPG 10, the intensity and / or amplitude of the pulses generated by the IPG 10, or any other details related to the generation of one or more pulses by the IPG 10. In some embodiments, the 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, information related to and / or used to control the generation of pulses by the IPG 10 may be stored in a memory.

[0044] In some embodiments, the pulse module 604 may include a stimulation circuit. The stimulation circuit may be configured to generate and deliver one or more stimulation pulses, and specifically may be configured to generate a voltage that drives a current that forms one or more stimulation pulses. The circuit may include one or more different components that may be controlled to generate one or more stimulation pulses, control one or more stimulation pulses, and / or deliver one or more stimulation pulses.

[0045] 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). During the life of the battery, the battery capacity and voltage will be depleted due to various types of use (e.g., transmitting stimulation pulses, self-discharge, energy consumption during communication with external devices, etc.). As the battery capacity is depleted, the energy available for operation of the IPG 10 will decrease over time. Battery capacity consumption may vary depending on treatment parameters, patient-specific usage, and various other factors. Battery capacity consumption is also affected by a variety of factors, which may include stimulation frequency, lower battery voltage, communication frequency, etc., which may affect or accelerate battery capacity consumption in different ways, while other factors (e.g., self-discharge, standard RF communication polling) may cause the battery capacity to be consumed at a substantially constant rate. Due to the various usage factors mentioned above, battery life may vary greatly.

[0046] Many systems rely on voltage monitoring to determine an estimate based on a standard battery discharge curve or a lookup table. Figure 7As shown, the voltage graph 700 of the battery discharge curve may be nonlinear and vary greatly. As shown, for most of the battery's life, the voltage may first drop, then rise steadily, after which the voltage begins to drop steadily and then drops exponentially at the end of the battery's life. Therefore, methods of determining battery capacity based on voltage are relatively inaccurate for most of the battery's life, and once the voltage drops significantly, there is only a limited time left before the battery ends and the battery must be replaced. While other methods attempt to determine battery capacity by estimating the battery discharge and subtracting these discharge estimates from the battery capacity at the beginning of use, these methods inherently contain errors. Over time, these errors cause the battery capacity estimates to become increasingly inaccurate, which may result in premature battery replacement or failure to provide adequate battery end-of-life notification. In addition, due to fundamental differences between these different methods, the battery capacity estimates of these methods may be completely different, so that clinicians or patients cannot reasonably rely on the remaining battery capacity estimates. Therefore, there is still a need for improved methods and systems to provide patients and clinicians with more accurate and reliable estimates of the remaining battery capacity. Based on the remaining battery capacity estimate, if the battery is unexpectedly depleted faster than expected, the patient and clinician can be notified so that the IPG can be replaced at the appropriate time. However, determining the remaining battery capacity is not an easy task.

[0047] As described herein, the remaining battery capacity can be a function of the manufacturer-provided derated battery capacity, system (e.g., stimulation circuitry including the hardware and software of the IPG 10) design parameters, patient impedance, and programmed stimulation parameters (e.g., current, pulse width, frequency, ramp duration, cycles, number of cathodes, etc.), or other parameters described herein.

[0048] Figure 7An example of a battery discharge curve 700 is shown, which shows the function of the battery voltage as a function of the battery capacity over the battery life (i.e., during use). The curve shown is applicable to Li-MnO2 primary cells. It will be appreciated that various other types of batteries include similar discharge curves, particularly lithium-based primary cells. In this discharge curve, after an initial phase in which the voltage remains relatively constant (except for the initial peak, drop, and gradual rise), the battery voltage begins to steadily decline as the battery capacity decreases. The total rate at which battery exhaustion occurs may vary depending on the degree of use over a period of time. Since the voltage remains relatively stable and includes a gradual rise during this initial phase (usually between four and six years), it is usually inaccurate to monitor the voltage during this period to determine the remaining battery capacity. However, a relatively accurate estimate of the remaining battery capacity can be provided by utilizing a method that takes into account various types of battery use occurring during this period relative to the battery capacity at the beginning of use. When the battery voltage steadily decreases as the battery life ends, voltage monitoring becomes a better indicator of the remaining battery capacity because the battery voltage can correspond to the characteristic discharge curve of the corresponding battery. For this battery, this steady decline typically lasts about 1 to 2 years, followed by a more dramatic decline in the late stage that lasts about 3 years. However, during the transition period between this initial and late stages, neither the first consumption-based method nor the second voltage-based method is completely accurate. Furthermore, given the fundamental differences between these methods, each will produce very different estimates, so neither method can reasonably be relied upon.

[0049] To overcome the above challenges and provide a more reliable and consistent estimate of remaining battery capacity over the life of the battery, the methods and systems herein utilize a method of dividing the battery discharge into at least different stages, such as Figure 7 These different stages include the initial stage PH1, the intermediate stage PH2 and the third stage PH3.

[0050] During the initial phase PH1, the curve 700 may be relatively flat at or near the nominal voltage. After the initial peak and drop, the voltage remains relatively stable and only exhibits a slight rise during this period. Once the voltage crosses the upper battery voltage threshold V 上限 , the voltage will begin to steadily decrease. In some embodiments, V 上限 Corresponds to about 95%-99%, typically about 96%, of the nominal battery voltage at full battery capacity (e.g., 2.974V for a battery with a nominal voltage of 3.1V). In the example shown, the battery voltage at full capacity varies between 2.94V and 3.3V, the nominal voltage of the battery is 3.1V, so the V cap is about 2.974V. When the voltage is maintained at V 上限When the battery capacity is greater than the lower battery capacity limit BC, for example, during the initial stage PH1, the remaining battery capacity may be estimated to be greater than the lower battery capacity limit BC. 下限 In some embodiments, during the initial phase PH1, the battery capacity can be accurately predicted based on energy consumption data related to the energy used by various loads on the battery during past use. 上限 Below this threshold voltage, it is observed that the remaining battery capacity can be estimated increasingly as a function of the battery voltage. Therefore, the method uses the equation to predict 上限 A threshold remaining battery capacity, which can be determined at least in part as a function of the battery voltage.

[0051] During the intermediate phase PH2, which occurs at V 上限 Threshold and battery voltage lower limit V 下限 The voltage decreases steadily between the thresholds, so the remaining battery capacity can be determined as a linear function of voltage. 下限 The threshold may be a voltage between 85% and 95% of the battery voltage at full battery capacity (e.g., approximately 93%, 2.87V, for a battery with a nominal voltage of 3.1V). However, as described above, based on V 上限 The estimated value determined by the battery consumption under the threshold may be different from the value based on V 上限 Therefore, in some embodiments, to provide a more consistent estimate of the remaining battery capacity, the method includes an equation that blends a first estimate based on battery consumption with a second estimate based on voltage. These estimates can be compared with the estimate at V 上限 The fully weighted first estimate under the threshold and the 下限 The fully weighted second estimates below the threshold are linearly mixed.

[0052] During the third phase PH3, the voltage is lower than V 下限 Threshold value, can be based on voltage alone to predict the remaining battery capacity, without any battery consumption data. In some embodiments, this determination uses a polynomial function of voltage. In some embodiments, the polynomial function can approximate a curve that follows the battery characteristic data for a given battery type.

[0053] Figure 8 The battery discharge curve is shown as a voltage graph 710 with the axes reversed to show the remaining battery capacity (eg, percentage of full capacity) as a function of voltage. Figure 8 Various different phases and sub-phases of a curve in a method that can be used to estimate remaining battery capacity are shown. Additional details, parameters and equations of the method are discussed in further detail below.

[0054] exist Figure 8During the initial phase PH1 shown, an initial peak and drop is followed by a slight rise in voltage, which may complicate the estimation because the same voltage occurs at different times. Therefore, the initial phase PH1 can be divided into a first sub-phase PHI-1 and a second sub-phase PHI-2. The first sub-phase PHI-1 distinguishes between a lower battery voltage occurring early in the battery life and the same lower battery voltage occurring later in the battery life. Therefore, different equations for the first sub-phase PHI-1 and the second sub-phase PHI-2 can be used to estimate the remaining battery capacity. The following discussion outlines the process of estimating the remaining battery capacity at different stages observed during the use of an implantable device (e.g., an IPG).

[0055] In one aspect, during an initial phase of battery use, a first consumption-based method (e.g., dead reckoning) that tracks or calculates battery energy consumed during use can be used to estimate remaining battery capacity. For example, the remaining battery capacity can be estimated by calculating the total battery energy consumption associated with energy usage of different IPG electrical loads and subtracting it from the battery capacity of a new battery (e.g., a derated capacity provided by the manufacturer). At a later stage, a second method based at least in part on battery voltage can be used to estimate remaining battery consumption.

[0056] The first consumption-based approach (e.g., dead reckoning) can accurately predict the remaining battery capacity when the actual battery energy consumption for different uses (e.g., self-discharge, communication with external devices, stimulation pulse delivery, etc.) is accurately tracked. However, the first approach may overestimate the battery consumption for past uses because the battery energy consumption for different uses may be based on conservative assumptions. Therefore, when the voltage drops to V 上限 The estimated remaining battery capacity is determined to be below the battery capacity lower limit BC. 下限 When the battery voltage reaches V 上限 (e.g., 2.974V) and / or at or below the upper battery capacity limit BC 上限 When the battery capacity is below the threshold (e.g., 70-80%, 75%), the estimate produced by the first consumption-based method may have a lower limit on the battery capacity BC 下限 The lower limit of the threshold (e.g., between 40-50%, about 46%). When the voltage then drops to V 上限 If the dead reckoning predicted remaining capacity is below the lower limit, the remaining capacity value is then calculated by a second voltage-based method. The second method can estimate the remaining battery capacity at least in part based on the battery voltage rather than the battery energy consumed by the IPG.

[0057] The process of estimating the remaining battery capacity may also take into account situations where the first method may be overly optimistic. In this case, the remaining battery capacity may be determined by combining or blending the results of the first method and the second method. In some embodiments, this blending or combination is applied during the intermediate stage PH2. Figure 8 The intermediate stage PH2 is a transition region characterized by the battery voltage V 上限 Threshold and V 下限 threshold (e.g., between 2.974 and 2.870 V). In some embodiments, the mixture may be such that: the remaining battery capacity V determined by the first method 上限 The residual capacity determined by the second method is dominated near the V 下限 The region around the threshold (eg, 2.870 V) dominates.

[0058] In some embodiments, unexpected battery depletion rates may occur. For example, a power system (battery, circuit, etc.) failure within the IPG may result in an unexpectedly faster battery depletion rate. This situation will cause the battery voltage to drop much earlier than estimated based on the first method or a combination of the first and second methods. Therefore, in the third phase PH3, it is characterized by the battery voltage being depleted to below V 下限 threshold (e.g., 2.870V), the remaining capacity may be based solely on the measured battery voltage. In other words, the remaining battery capacity calculated using the first method (e.g., the dead reckoning method) may be ignored at this stage. Thus, regardless of the calculation based on the usage of the implantable device, the battery unexpectedly depleting faster than expected will be indicated to the patient.

[0059] Set forth below are descriptions of various operations and methods that use battery power and result in battery depletion. All or some of these battery power usages may be considered when determining the estimated remaining battery capacity from the battery depletion data. Such usages may include the following:

[0060] In some embodiments, the fixed battery energy usage dataset 921 includes a first energy consumption dataset associated with battery energy consumed or lost due to battery self-discharge (use 1). Battery self-discharge can be energy consumed inside the battery. Since self-discharge occurs inside the battery, the first use may not be measured outside the battery. In some embodiments, parameters used to determine the first energy consumption dataset may include, but are not limited to, annual self-discharge (e.g., in percentage), battery derated capacity (e.g., in joules), or other relevant factors. For example, battery self-discharge can be calculated in joules / day based on the derated capacity and the self-discharge rate.

[0061] In some embodiments, the fixed battery energy usage data set 921 includes a second energy consumption data set associated with the battery energy consumed by the communication polling performed at the first scan rate to determine whether the external device is requesting communication (use 2). In some embodiments, the second energy consumption data set includes the battery energy consumed during the periodic scan performed by the IPG to determine whether the external device (e.g., the remote control 70 or the clinician programmer 60) is requesting communication with the IPG. Energy consumption over time depends on several factors and may be difficult to model analytically. Therefore, the battery energy consumption can be determined using parameter values ​​from actual measurements. The parameters used to determine the second energy consumption data set may include, but are not limited to: the energy used by the battery to receive radio frequency (RF) signals (e.g., in joules μ / scan), the energy margin reserved for the potential increase in energy required for each future scan (e.g., in μ Joules / scan), the modeled energy extracted from the battery for receiving RF signals (in μ Joules / scan), the RF scan interval (e.g., seconds), the number of RF scans per day, or other related parameters. Therefore, the second energy consumption data set may include the total polling energy calculated based on the above parameters.

[0062] In some embodiments, the fixed battery energy usage data set 921 includes a third energy consumption data set, which is associated with the battery energy consumed by the radio frequency communication involving communication with the external device (using 3). In some embodiments, the third energy consumption data set includes the energy consumed when the IPG (e.g., 10) has a communication event with the external device (e.g., the remote control 60 or the clinician programmer 70). The energy consumption over time depends on several factors and may be difficult to model analytically. Therefore, when determining the modeling parameters for analytical modeling or empirical modeling, the actual measured values ​​at different stages of the communication can be used to calculate the total value of energy consumption. For example, the parameters used to determine the third energy consumption data set may include, but are not limited to: the energy consumed by a predetermined amount (e.g., in seconds) of conversation (e.g., in millijoules), the energy margin provided for future changes (e.g., in millijoules), the number of conversations per event, the number of events per year, the number of scans calculated proportionally per day, or other factors. Therefore, the third energy consumption data set may include the total proportional energy (e.g., in joules / day), which can be calculated based on the above parameters.

[0063] In some embodiments, the fixed battery energy usage data set 921 includes a fourth energy consumption data set associated with battery energy consumed by communication at a second scan rate to determine whether an external device is requesting communication (use 4). In some embodiments, the implantable device can be configured to poll for subsequent conversations with the external device at the second scan rate. For example, after an RF conversation session, within a subsequent predetermined time period (e.g., 60 seconds), the IPG can poll for subsequent conversations with a patient remote control or clinician programmer at a faster rate (e.g., a cycle of 4.6 seconds) than the normal polling rate (e.g., a cycle of 16 seconds). This functionality may be intended to make the IPG more responsive to immediate subsequent conversations. Parameters in the second energy consumption data set related to energy usage of RF polling can be used to develop a model of such communication. In addition, the parameters used to determine the fourth energy consumption data set may include, but are not limited to: a second scan interval (e.g., in seconds), a duration after interaction with an external device (e.g., in minutes), the number of scans per event, the battery energy consumed by receiving RF signals (e.g., in μJoules / scan), the battery energy consumed by receiving RF signals per event (e.g., in milliJoules), the memory write energy per event (e.g., in milliJoules), the number of events per year, the number of events per day allocated proportionately, or other parameters. Therefore, the fourth energy consumption data set may include the total RF communication polling energy (in Joules / day) allocated proportionately calculated based on the above parameters.

[0064] In some embodiments, the fixed battery energy usage data set 921 includes a fifth energy consumption data set, which is related to the battery energy consumed by the housekeeping tasks performed by the software in the implantable device (using 5). In some embodiments, the fifth energy consumption data set includes the energy consumption of countless housekeeping tasks regularly performed by the IPG software. This energy consumption can be modeled. The values ​​used in the model can be determined from the measured data. The measured values ​​can be modeled enough to allow future software versions to add additional housekeeping tasks and therefore use more energy. The parameters used to determine the fifth energy consumption data may include, but are not limited to: the energy consumed by the housekeeping tasks from the battery (for example, in joules / day), the energy margin or other parameters that allow the energy usage to increase when the future software is changed. Therefore, the fifth energy consumption data can be the total energy in joules / day calculated based on the above parameters.

[0065] In some embodiments, the fixed battery energy usage dataset 921 includes a sixth energy consumption dataset (use 6) related to battery energy consumed by quiescent current present in the implantable device. For example, the sixth energy consumption dataset includes the energy used to provide the quiescent current that the IPG has been consuming from the battery after the battery is connected to the circuit board of the IPG. Parameters used to determine the sixth energy consumption dataset may include, but are not limited to: the system quiescent current drawn from the battery (e.g., in μA), the voltage at which the quiescent current discharge occurs (e.g., in V), or other parameters. Therefore, the sixth energy consumption dataset can be the total energy in joules / day calculated based on the above parameters. In some embodiments, the total daily energy consumed from the battery (e.g., in joules / day) can be calculated as the sum of the first to sixth energy consumption datasets.

[0066] In some embodiments, the fixed battery energy usage dataset 921 includes a seventh energy consumption dataset associated with battery discharge or energy lost due to field discharge experienced by the battery prior to connection to an implantable device (e.g., IPG 10) (use 7). In some embodiments, the battery self-discharge period of the IPG can be divided into the following stages: (i) self-discharge of the battery from the date of manufacture; (ii) daily energy consumption experienced by the IPG circuit once the battery is connected (e.g., the first to sixth energy consumption datasets); (iii) after assembly, the IPG is tested at the IPG assembler; and (iv) after sterilization, the packaged IPG is tested at another manufacturer's location. The duration of each stage within 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 duration characterized by a time period from the battery manufacturer to connection to the IPG circuit (e.g., in months), a second duration characterized by a time period from the battery being connected to the IPG assembly (e.g., in months), a third duration characterized by a time period from the IPG assembly to sterilization and preparation for shipment (e.g., in months), and a fourth duration characterized by a time period from preparation for shipment to implantation (e.g., in months). Therefore, the total duration from battery manufacturing to implantation (e.g., in months) can be calculated as the sum of the first duration to the fourth duration. Similarly, the duration from the battery being connected to the circuit to implantation can be calculated as the sum of the second duration to the fourth duration. Based on these durations (e.g., the first to fourth durations) and the battery self-discharge parameter (e.g., in joules determined during the first energy consumption data set), the energy consumed from the battery over different durations can be determined.

[0067] In addition to the battery energy consumed during the first to fourth durations, an energy margin (e.g., in Joules) may also be included to allow for future test device upgrade increases. In addition, the energy used during packaged IPG testing at the IPG manufacturer may also be included (e.g., in Joules). Based on the above energy calculations, the total energy used on self-discharge may be calculated as the sum of the energy consumed during the first to fourth durations, the energy margin, or other parameters. These above-mentioned energy values ​​related to battery discharge on self-discharge may be included in the seventh energy consumption data set.

[0068] In some embodiments, the fixed battery energy usage dataset 921 includes an eighth energy consumption dataset that is related to battery energy consumed due to communication with external devices during implantation of the implantable device (use 8). For example, the IPG 10 communicates with the clinician programmer 70 during implantation. After implantation, a follow-up visit with the clinician is typically performed. It is also expected that after the IPG is implanted, the patient may enter the clinician's office several times, such as for mid-term examinations and examinations at the end of the IPG's life. The energy used when the IPG communicates with the clinician programmer is similar to the energy used when the IPG communicates with the patient remote control. The energy usage of a communication session between the IPG and the patient remote control can be modeled as a predetermined time period (e.g., 30 seconds) for the communication session.

[0069] The parameters used to determine the eighth energy expenditure data may include, but are not limited to: (i) the total energy (e.g., in millijoules) consumed by a single session with a first external device (e.g., a patient remote control) within a predetermined time period (e.g., 30 seconds), (ii) the number of equivalent sessions between the first external device (e.g., a patient remote control) and a second external device (e.g., a clinician programmer) within a second time period (e.g., 45 minutes) of the implant surgery, (iii) the number of equivalent sessions between the first external device (e.g., a patient remote control) and a second external device (e.g., a clinician programmer) within a third time period (e.g., 10 minutes) of a subsequent surgery, and (iv) the number of equivalent sessions between the first external device (e.g., a patient remote control) and a second external device (e.g., a clinician programmer) within a third time period (e.g., 10 minutes) of a subsequent surgery. The energy consumption data 921 may be used to estimate the energy consumption of the eighth energy consumption data set. Thus, the fixed energy consumption data 921 may include several different types of energy consumption data and related parameters.

[0070] Similarly, active battery energy consumption data set 922 includes data related to parameters that affect the energy required to deliver stimulation pulses. The energy consumed by the battery depends on parameters related to the delivery of stimulation pulses (e.g., stimulation pulse frequency, current amplitude, etc.), hardware and software components, patient tissue characteristics, or other parameters. In some embodiments, the energy associated with the delivery of stimulation pulses can be determined using a system model of an implantable device (e.g., IPG 10).

[0071] In some embodiments, a model of the IPG hardware and software may be developed to determine the energy consumption associated with the delivery of stimulation pulses. For example, the energy model may be a function of the energy consumption of the central processing unit (CPU) where the IPG software resides and another energy consumption of the analog circuits used to generate the stimulation pulses. The CPU energy consumption may be characterized by a ninth energy consumption data set, while the hardware energy consumption may be characterized by a tenth energy consumption data set, both of which will be discussed in further detail below.

[0072] In some embodiments, a ninth energy consumption data set includes parameters related to battery energy consumed by a processor, controller, or central processing unit (CPU) to release or activate a stimulation pulse (using 9). Parameters related to CPU energy consumption may have a weak relationship with pulse duration. In addition, CPU energy consumption may not depend on the energy used to transmit the stimulation pulse. Therefore, the determined CPU energy consumption can be regarded as a fixed energy used for each stimulation pulse.

[0073] The parameters used to determine the ninth consumption data may include, but are not limited to, CPU startup time (μSec) and CPU startup current (mA). The startup energy consumption (μJoules / pulse) of each stimulation pulse may be calculated as a function of the CPU startup time and current. In addition, the parameters may include, but are not limited to, CPU processing duration (μSec) and CPU processing current (mA). The energy consumption (μJoules / pulse) during CPU processing may be calculated as the processing duration and current.

[0074] In some embodiments, the stimulation pulse can be transmitted in stages. In this case, the parameters may further include, but are not limited to, the first stage duration (μSec) of the stimulation pulse transmission and the CPU current (mA) during the first stage. The energy consumed during the first stage of the stimulation pulse transmission (μJoules / pulse) can be calculated as a function of the first stage duration and current. In addition, the parameters may include, but are not limited to, the inter-stage delay (μSec), the second stage duration (μSec), the CPU current during the inter-stage delay and the second stage transmission current (mA). The energy consumed during the inter-stage delay and the second stage (μJoules / pulse) can be calculated as a function of the above parameters. Therefore, the above parameters and the CPU energy consumption associated with these parameters can be used to estimate the CPU energy consumption. Therefore, the ninth consumption data set may include parameter values ​​related to startup, processing, different stages and corresponding energy consumption. The total CPU energy consumption can also be calculated as the sum of each energy consumption (μJoules / pulse) during startup, processing and different stages, and is included in the ninth energy consumption data set.

[0075] In some embodiments, the tenth energy consumption data set includes parameters related to battery energy consumed by the analog circuitry of the IPG to deliver stimulation pulses (using 10). Parameters used to determine the tenth energy consumption data set may include, but are not limited to: a specified stimulation current amplitude, a specified stimulation pulse width, a specified stimulation frequency (e.g., 14 Hz), a resistive component of the patient (e.g., tissue resistance), a specified duty cycle, and a number of pulses per day.

[0076] The tenth energy consumption data set parameter may include the maximum possible current (mA) for the stimulation pulse delivery. The voltage across the patient may be limited to a specific voltage (e.g., 9 volts.) The double layer capacitor (e.g., 0.47 μF) at the electrode / tissue interface may establish a voltage in the first phase of the stimulation pulse. Therefore, the maximum current that can be delivered to the patient may depend on the patient impedance and the pulse duration. Therefore, the maximum possible current can be calculated based on the specific voltage, patient impedance and resistance, and pulse duration.

[0077] The tenth energy consumption data set parameter may include a limited stimulation current amplitude (μA) for stimulation pulse delivery, which may be a lower limit of the programmed current and the maximum possible current calculated above. The tenth energy consumption data set parameter may include a stimulation voltage (mV) for the patient's resistive portion, which may be the voltage at the beginning of the first phase, assuming that the patient's resistive portion is a pure resistor. The tenth energy consumption data set parameter may include a voltage accumulation of the patient's portion, thereby acting as a capacitor during the first phase of the stimulation pulse delivery. The tenth energy consumption data set parameter may include a voltage drop due to switches, sense resistors, and system circuit impedance. Due to internal component impedance, the voltage inside the IPG may drop, depending on the current. The tenth energy consumption data set parameter may include the total stimulation voltage required by the stimulation power supply, and may be calculated as the sum of the stimulation voltage, voltage accumulation, and voltage drop.

[0078] In some embodiments, the hardware components of the IPG may include power converters, such as buck (e.g., lowering voltage) and / or boost (e.g., raising voltage) power supplies. Thus, the tenth energy consumption data set parameter may include minimum buck and maximum boost specifications. The voltage used to transmit the stimulation pulse may be bounded. The lower limit may correspond to the minimum voltage that the buck power supply can transmit. The upper limit may correspond to the maximum voltage that the boost power supply can transmit. In some embodiments, depending on the voltage requirements during the transmission of the stimulation pulse, a buck or boost power supply will be engaged. In addition, the tenth energy consumption data set parameter may include power converter efficiency, which may depend on buck and / or boost. The tenth energy consumption data set parameter may include the energy transmitted by the stimulation power supply in the first phase. The transmitted energy may be calculated based on the stimulation current, the stimulation voltage, and the duration of the first phase. The tenth energy consumption data set parameter may include the energy (μ Joules) consumed from the battery for each pulse based on the efficiency of the buck and / or boost converter.

[0079] The tenth energy consumption data set parameter may include the quiescent current (μA) consumed by the buck and / or boost converters. For example, for the buck converter, the quiescent current lasts for the entire duration of the stimulation pulse. For the boost converter, the quiescent current only lasts for the duration of the first stage. In addition, these parameters may include the quiescent current energy (μJoules) per pulse, which may be calculated based on the quiescent current of the converter, the duration of the quiescent current, and the stimulation frequency. The tenth energy consumption data set parameter may include the total analog circuit energy, which may be calculated as the sum of the energy consumed from the battery for each pulse (μJoules) and the quiescent converter energy (μJoules) for each pulse. Therefore, the active battery energy consumption data set 922 may include the total energy associated with the delivery of stimulation pulses, which may be calculated as the sum of the energy consumption associated with the digital (e.g., CPU) and analog circuits delivered for each stimulation pulse.

[0080] In some embodiments, based on the battery energy consumption data set and related parameters, the life span or expected life span (e.g., in days) of the battery can be calculated. In one aspect, the battery life span can be calculated by the following formula:

[0081] Lifespan (days) = available battery capacity / daily usage

[0082] Available battery capacity = derated battery capacity - (use 7 + use 8)

[0083] Daily use = (use 1 to use 6) + (E*F*C)

[0084] E = Energy Factor = (Use 9 + Use 10) * (Daily 14Hz Pulse Number)

[0085] F = frequency factor = stimulation frequency / 14

[0086] C = Cycle Factor (C = 1 if no cycle is used) =

[0087]

[0088] As described above, the life of the battery 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, energy factor (E), frequency factor (F) and cycle factor (C) in the first to sixth energy consumption data sets. 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., 14Hz per day). The frequency factor (F) can be a function of the stimulation frequency and a given frequency (e.g., 14Hz). The frequency factor (F) takes into account the effect of using a stimulation frequency other than a given frequency (e.g., 14Hz) on energy. The cycle factor (C) takes into account the cycle of starting, ending and accelerating stimulation using additional energy. This can be explained by multiplying the acceleration time when calculating the cycle factor (C).

[0089] Fig. 9 800 includes estimating the remaining battery capacity based on the battery energy consumption and battery voltage at different stages of the 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. Therefore, the method 800 can involve collecting and analyzing data related to battery energy usage associated with the IPG, measuring the battery voltage from the IPG, and using this data to estimate the remaining battery capacity.

[0090] Most of the battery energy consumed by the IPG can be used after being converted to a fixed voltage. In other words, most of the energy consumed may be independent of the battery voltage. As the remaining battery capacity is depleted, the battery voltage decreases and falls (e.g., as described in the description of the battery voltage). Figure 7-Figure 8 As discussed above). For a given amount of energy, more current is drawn from a battery as it depletes. Therefore, a battery's ability to deliver energy (e.g., in joules) may be favored over its ability to deliver current (amps). Thus, even if a battery manufacturer has rated batteries based on their current delivery capabilities (mAh), method 800 may determine battery capacity based on energy (e.g., in joules). Although the method determines capacity based on energy (e.g., in joules), the method may also be applied and converted to measure capacity in watt-hours, milliampere-hours, coulombs, or a percentage of rated or maximum capacity. Method 900 is discussed in further detail below.

[0091] In example method 900, step 902 involves establishing communication between an implantable device and an external device. In some embodiments, the implantable device may be an IPG for any neurostimulation therapy (e.g., brain, muscle, spine, sacral, etc.). In some embodiments, the external device may be a clinician programmer and / or remote control (e.g., Figure 1 The remote control 70 may be configured to transmit and control parameters related to the delivery of stimulation pulses by the IPG. In some embodiments, a user (e.g., a patient) can change the stimulation parameters set by the clinician programmer 60 to provide higher or lower stimulation pulses. Therefore, the actual battery usage may be higher or lower than the estimated battery usage for the program set by the clinician programmer 60.

[0092] Step 904 involves receiving information from an implantable device (e.g., IPG 10), the information including battery energy consumption data BC 920 and battery voltage V 930. In some embodiments, the battery energy consumption data 920 includes data related to different battery energy consumptions that lead to battery capacity depletion. In some embodiments, the battery energy consumption data includes fixed battery energy usage data 921 related to a fixed energy consumption amount of a battery used for a specific purpose. In some embodiments, the battery energy consumption data set includes active battery energy consumption data 922 related to stimulation pulse delivery. The fixed battery energy usage data set may include, but is not limited to, any of the following: battery energy consumption related to at least one of fixed battery discharge, periodic communication with an external device, daily tasks related to software running within the implantable device, or quiescent current consumed by the implantable device, or any combination thereof.

[0093] Step 906 involves determining an estimated remaining battery capacity based on the received information. In some embodiments, the estimated remaining battery capacity may be determined in different ways for different stages observed during use. In some embodiments, at step 906, as shown in FIG. Fig.10 The estimated remaining battery capacity is determined as shown and will be discussed in further detail below. The external device then outputs the battery capacity estimate 940 to the user, such as on a graphical user interface display.

[0094] In some embodiments, communication is established when the patient meets with the clinician, at which time the clinician programmer queries the IPG to determine an estimate of the remaining battery capacity (e.g., through direct communication between the IPG and the clinician programmer). The estimate can be expressed in any suitable manner (e.g., as a percentage of the total capacity, or as a remaining battery life (weeks / months / years / etc.)). In some embodiments, the clinician programmer determines the battery capacity estimate in the session based on the information obtained and indicates the estimate to the clinician, but does not otherwise store or maintain the estimate on the clinician programmer or patient profile. Since the estimate is not stored or maintained, the clinician programmer cannot update the estimate. In such an embodiment, if a new or updated estimate is needed, the clinician programmer must initiate a new communication session and receive updated data and perform a new estimate according to the method detailed herein. In some embodiments, the battery capacity estimate can be determined and / or displayed on a patient remote control or other electronic device that can communicate with the IPG.

[0095] Fig.10 An exemplary method 1000 for determining an estimate of remaining battery capacity according to some embodiments is shown. The method includes steps 1001, 1002, and 1003, which correspond to different stages of use during the battery's service life (e.g., an initial stage, an intermediate stage, and a third stage). In some embodiments, each of steps 1001, 1002, and 1003 is performed at different times, rather than simultaneously or sequentially. In some embodiments, steps 1001, 1002, and 1003 can be selectively performed based on trigger conditions defined as a function of battery voltage and / or remaining battery capacity according to voltage and capacity thresholds described herein. Step 1001 is performed during an initial stage of use, where the estimated remaining battery capacity is determined based on a battery energy consumption dataset when the battery is first put into use relative to the battery capacity when fully charged. The full capacity value can be reduced to provide a more conservative method for estimating battery capacity, thereby ensuring sufficient time to notify the replacement of the battery. Step 1002 is performed during an intermediate stage of use, where the estimated remaining battery capacity is based on a combination of the battery energy consumption dataset and voltage. Step 1003 is performed during the third phase of use, where the estimated remaining battery capacity is determined based on the voltage. When method 1000 is performed periodically over the life of the device (e.g., during annual or biennial clinician / patient visits), different steps will be performed at different times during the corresponding phases.

[0096] In one aspect, the voltage range assumes a nominal voltage for the battery and an upper limit for the battery voltage and a lower limit for the battery voltage indicating end of life. In the exemplary embodiment disclosed herein, for a battery with a nominal voltage of 3.1V, the upper limit of the voltage is approximately 3.3V and the lower limit is approximately 2.3V, which indicates end of life. The following represents example equations for the battery capacity estimation method described herein. It will be understood that these equations are exemplary and the thresholds referenced are specific values ​​associated with the exemplary battery described herein, and these concepts can be modified and applied to parameters of various other batteries as needed.

[0097] In this initial stage ( Figure 8 PH1 in, the estimated remaining battery capacity can be determined based on the battery energy consumption data relative to the total capacity when fully charged. For example, the cumulative battery energy consumed by the IPG determined based on the data can be subtracted from the total capacity of the battery. The method of using the battery energy consumption data set can be referred to as a first method or a dead reckoning method, which involves tracking energy consumption for different uses. For example, the battery energy consumption data can include calculating energy consumption for different types of uses, such as the first energy consumption data set to the tenth energy consumption data set, as previously described.

[0098] In some embodiments, the initial phase can be divided into a first sub-phase (e.g., Figure 8 PH1-1 in the second substage (e.g., Figure 8 In the first sub-stage (e.g., PH1-1), the first method may be applied to determine the remaining battery capacity. In the second sub-stage (e.g., PH1-2), the remaining battery capacity may be calculated by the first method described above, but if it is too pessimistic, a specific value, such as a lower capacity threshold, may be assigned to the estimated value. For example, applying the first method in the second sub-stage may predict a lower remaining battery capacity than the actual remaining battery capacity. Therefore, a lower limit may be applied to the remaining battery capacity during the second sub-stage.

[0099] In some embodiments, during the first sub-stage, when the capacity is greater than the battery capacity upper limit BC 上限 When the battery capacity reaches a certain threshold (e.g., 70-50%, approximately 75% of the full capacity), the estimated remaining battery capacity is calculated by the first method. In some embodiments, the estimated remaining battery capacity can also be calculated based on the battery voltage lower limit V 下限 A further check of the voltage at a threshold (e.g., 85-95%, approximately 92% of the nominal battery voltage) determines the first sub-stage. In some embodiments, for a battery with a nominal voltage of 3.1V, V 下限 The threshold is 2.87V.

[0100] refer to Figure 8 As an example, when the voltage is greater than V 下限When the remaining battery capacity (eg, determined by the first method) is greater than 75%, the battery can be considered to be in the first sub-phase PH1-1 and the estimated battery capacity can be considered to be an accurate prediction.

[0101] review Figure 8 During the second sub-phase (eg, PH1-2), a check may be performed to determine whether the voltage is greater than the battery voltage upper limit V 上限 Threshold (e.g., 95-99% of nominal voltage, about 96%, 2.974V for a battery with a 3.1V nominal voltage). In some embodiments, a check may be performed to determine whether the estimated remaining battery capacity (e.g., determined by the first method) is less than BC 上限 The threshold is greater than the battery capacity lower limit BC 下限 Threshold. For example, BC 上限 The threshold value may be between 70%-80%- (eg, 75%-) of full battery capacity, and BC 下限 The threshold may be between 40%-50% of full battery capacity (eg 45%).If these checks are met, the remaining battery capacity determined by the first method may be considered an accurate prediction.

[0102] by Figure 8 For example, during the second sub-phase PH1-2, when the voltage is greater than V 上限 The threshold (e.g. 2.974V) and / or the estimated remaining battery capacity is below BC 上限 When the value estimated by the first method is less than BC 下限 threshold, the estimated remaining battery capacity determined by the first method may be considered too pessimistic and may be lowered to the battery capacity BC 下限 Threshold, thereby discarding the values ​​estimated by the first method.

[0103] In the intermediate stages of use (e.g. Figure 8 During the PH2) in the intermediate stage, the estimated remaining battery capacity can be determined based on the 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 linearly combined, nonlinearly combined, weighted combined, or combined by various other methods. The present disclosure is not limited to a specific combination method.

[0104] In some embodiments, during the intermediate phase, a first estimate based on a first method utilizing battery energy consumption data and a second estimate based on a second method utilizing voltage are linearly combined such that the battery energy consumption data set 920 is fully weighted at the beginning of the intermediate phase and the voltage is fully weighted at the completion of the intermediate phase. In some embodiments, the first estimate may be a lower limit value from subphase PH1-2.

[0105] An example method of estimating the remaining battery capacity includes: (i) determining the remaining battery capacity as a first function f1 of the battery voltage; and (ii) determining the remaining battery capacity based on the battery energy consumption data set 920, as previously described. The first function f1 may be an empirical equation based on measured and / or simulated data during battery use. For example, the first function f1 may be a1*V b +b1, where a1 and b1 are fitting coefficients, V b is the battery voltage. The measured data and / or simulated data include the battery voltage, and the battery capacity measured / simulated during use of the IPG. In some embodiments, the measured data may be collected from an IPG implanted in a patient. In some embodiments, a model associated with the IPG may be used to generate the simulated data. The simulation model of the IPG may include a physical model of energy usage of IPG components (e.g., including hardware and software), simulated behavior of the IPG in use, and other models configured to simulate IPG behavior and battery depletion.

[0106] by Figure 8 For example, the intermediate stage PH2 corresponds to a battery voltage 930 between the upper and lower thresholds (e.g., between 2.974V and 2.870V). In some embodiments, if the remaining battery capacity (BC) determined by the first method DR ) is less than the remaining battery capacity (BC) determined based on the voltage BV ), the estimated remaining battery capacity 940 is designated as BC BV Otherwise, the estimated remaining battery capacity 940 may be calculated as applying function f1 (e.g., 1*V b +b1, where a1 = 1.4376082; b1 = -3.8259548) (e.g. BC BV ) and the results of the first method using battery energy consumption data (e.g. BC DR ). For example, the linear combination function can use the second function f2 (which is expressed as 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 Therefore, the estimated remaining battery capacity 940 in stage 2 is equal to f2*BC BV+(1-f2)*BC DR .

[0107] In the third stage (see Figure 8 PH3), the estimated remaining battery capacity 940 can be based on the voltage 930. In some embodiments, when the voltage 930 is lower than the battery voltage lower limit V 下限 The third stage occurs when the V 下限 The threshold can be between 85-95% of the battery voltage at full battery capacity. For example, for a battery with a nominal voltage of approximately 3V, V 下限 The threshold can be between 2.8-2.9V.

[0108] In some embodiments, a voltage V b The third function f3 is used to calculate the estimated remaining battery capacity 940. For example, the third function f3 may be a polynomial function, expressed as Where cl-c5 may be fitting coefficients determined based on measured and / or simulated data, as previously described. It will be appreciated that the present disclosure is not limited to a particular order of polynomials, and any other polynomial function of voltage may be used. As an example, the estimated remaining battery capacity 940 in phase PH3 may be calculated as:

[0109] In some embodiments, values ​​for different parameters used to determine remaining battery capacity may be provided by the manufacturer, measured, designed, assumed, and / or calculated. For example, manufacturer values ​​may provide self-discharge rate and derated battery capacity. Measured values ​​may include measured values ​​or may be obtained experimentally. Such experimentally based values ​​may be used when an accurate analytical model is not available. In order to use measured values ​​in a model, margins may be added to allow for measurement errors and future hardware and / or software changes that may increase power usage. Design values ​​may include values ​​that are part of the hardware or software design. Assumed values ​​may include values ​​that are assumed (or sometimes estimated) based on experience or observation, such as the frequency with which a patient uses their patient remote control to connect to the IPG.

[0110] It is to be understood that the embodiments described above in the specification and drawings are to be regarded as illustrative rather than restrictive. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the disclosure as set forth in the claims.

[0111] In the context of describing the disclosed embodiments (especially in the context of the following claims), the use of the terms "a", "an" and "the" and similar references should be interpreted as covering the singular and plural, unless otherwise specified herein or the context is clearly contradictory. The description of the numerical range herein is intended only as a shorthand method for individually referencing each individual value in the range, unless otherwise specified herein, and each individual value is incorporated into the specification as if it were individually described. All methods described herein can be performed in any suitable order, unless otherwise specified herein or the context is clearly contradictory. It should be understood that terms such as "first", "second", "third" and the like do not necessarily limit the embodiments of the present disclosure to any specific configuration or direction. As used herein, the term "approximately" means + / -10% of a specified value. As used herein, the term "data" should be understood to mean a cumulative value of one or more values, a set of values, historical values ​​or past values. In addition, unless otherwise explicitly stated, disjunctive language such as phrases "at least one of x, y or z" should be understood in the context as generally used to indicate that an item can be x, y or z, or any combination thereof (e.g., x, y and / or z). Thus, such disjunctive language is generally not intended to imply that certain embodiments require that each of x, y, and z be present, nor should it be implied that certain embodiments require that each of x, y, and z be present.

[0112] After reading this specification, a person of ordinary skill in the art can clearly understand the various changes of the embodiments of this invention. The inventor hopes that a person of ordinary skill in the art can adopt these changes as appropriate, and the inventor hopes that the present disclosure is implemented in embodiments different from those specifically described herein. Therefore, the present disclosure includes all modifications and equivalents of the subject matter described in the appended claims as permitted by applicable law. In addition, unless otherwise specified herein or the context is clearly contradictory, the present disclosure covers any combination of the above elements in all possible variations thereof.

Claims

1. A neural stimulation system, comprising: an implantable pulse generator (IPG) including a battery, and an external device configured to communicate with the IPG; wherein the IPG is used during a time period including an initial stage and a later stage; wherein the external device is configured to receive information from the implantable device, the information comprising battery energy consumption data and battery voltage; Wherein, the external device includes a processor configured to determine an estimate of battery capacity; wherein, in 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 using the IPG; and Wherein, in the later stage, the processor is configured to determine an estimated value of the battery capacity based on the battery voltage.

2. The system according to claim 1, wherein: The usage time period of the IPG includes an intermediate stage between the initial stage and the later stage, and wherein, in 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.

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

4. The system according to claim 2, wherein: In the intermediate stage, the estimated value of the battery capacity is determined by 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.

5. The system according to claim 4, wherein: The first and second battery capacity results are linearly combined.

6. The system according to claim 4, wherein: In the intermediate stage, the first and second battery capacity estimation results are linearly combined so that the first battery capacity result based on energy consumption data is fully weighted when the intermediate stage starts and the second battery capacity result based on voltage is fully weighted when the intermediate stage is completed.

7. The system according to claim 4, wherein: The intermediate stage occurs between the battery voltage upper limit threshold and the battery voltage lower limit threshold.

8. The system according to claim 7, wherein: The battery voltage upper limit threshold is between 95% and 99% of the battery nominal voltage when fully charged, and the battery voltage lower limit threshold is between 85% and 95% of the battery nominal voltage.

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

10. The system according to claim 1, wherein: In the later stage, ie when the voltage is below a lower battery voltage threshold, the estimate is based on the voltage.

11. The system according to claim 10, wherein: In the later stage, the estimate is based on a polynomial function of the voltage derived from the battery characteristics.

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

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

14. The system of claim 1, wherein: In the initial stage, the estimated value of the battery capacity includes subtracting the accumulated energy discharged from the battery from the battery capacity at the beginning of use.

15. The system of claim 14, wherein: The battery energy consumption data includes cumulative energy discharged from the battery.

16. The system of claim 14, wherein: The accumulated energy discharged from the battery is determined by the processor and stored on the IPG.

17. The system of claim 14, wherein: The accumulated energy released from the battery is determined by the external device and is based on the battery energy consumption data received from the IPG.

18. The system of claim 1, wherein: The initial stage includes a first sub-stage when the estimated value of the battery capacity is higher than the battery capacity upper limit threshold and a second sub-stage when the estimated value of the battery capacity is lower than the battery capacity upper limit threshold, and wherein, in the first sub-stage, the estimated value of the battery capacity is determined based on the battery energy consumption data; and Therein, in the second sub-stage, the estimated value is determined based on the battery energy consumption data and has a minimum value set at a battery capacity lower limit threshold.

19. The system of claim 18, wherein: The battery capacity upper limit threshold is approximately 75% of the battery capacity at the beginning of use.

20. The system of claim 18, wherein: In the second sub-phase, the voltage is greater than the battery voltage upper limit threshold.

21. The system of claim 20, wherein: The battery voltage upper limit threshold is between 95% and 99% of the battery nominal voltage.

22. The system of claim 21, wherein: The battery voltage upper limit threshold is approximately 2.974V, wherein the battery nominal voltage is approximately 3.1V.

23. The system of claim 18, wherein: In the second sub-phase, the estimated battery capacity is less than the battery capacity upper limit threshold and greater than the battery capacity lower limit threshold.

24. The system of claim 23, wherein: The battery capacity upper limit threshold is between 70% and 75% of the battery capacity at the beginning of use, and the second battery capacity threshold is between 40% and 50% of the battery capacity at the beginning of use.

25. The system of claim 1, wherein: The battery is a non-rechargeable primary battery.

26. The system of claim 25, wherein: The battery is a lithium manganese dioxide (Li-MnO2) battery.

27. The system of claim 1, wherein: The battery energy consumption data includes active battery energy consumption data associated with stimulation pulse delivery; and stationary battery energy usage data associated with stationary energy consumption.

28. The system of claim 27, wherein: The fixed battery energy usage data includes battery energy usage related to at least one or any combination of the following: Fixed battery discharge; periodic communication with said external device; One or more housekeeping tasks associated with the execution of software within the IPG; as well as The quiescent current consumed by the IPG.

29. The system of claim 28, wherein: The fixed battery energy usage data set includes at least one or any combination of the following: a first usage data set related to energy used by self-discharge of the battery; a second usage data set associated with communications polling at a first scanning rate to determine whether the external device is requesting energy for communications; a third usage data set related to energy used for radio frequency communications involving communications with the external device; a fourth usage data set related to energy used by communicating at a second scanning rate to determine whether the external device is requesting communication; a fifth usage data set related to energy used by one or more housekeeping tasks; a sixth usage data set related to energy used by quiescent current consumed by the IPG; a seventh usage data set related to energy used by a field discharge experienced by the battery prior to connection to the IPG; as well as An eighth usage data set is associated with energy used to communicate with the external device while the IPG is implanted.

30. The system of claim 27, wherein: Activity usage data includes: a ninth usage data set relating to energy usage by the processor for stimulation pulse delivery; and A tenth usage data set relating to energy used by stimulation generation circuitry of the IPG for delivery of stimulation pulses.

31. An external device communicatively coupled to an implantable device for placement in a patient, wherein: The implantable device includes a battery, and the external device includes: a graphical user interface configured to facilitate programming and monitoring of the implantable device; and A processor coupled to a storage device, wherein the processor is configured to: establishing communication with the implantable device; receiving information from the implantable device, the information comprising battery energy consumption data and battery voltage; and determining an estimate of the battery capacity based on the received information; wherein the processor is configured to determine an estimate of the capacity of the battery during an initial stage of battery use using the accumulated battery energy consumption data relative to the total capacity of the battery at the beginning of battery use; wherein the processor is configured to determine an estimate of the battery capacity during an intermediate stage of use based on a combination of a cumulative battery energy consumption data set and a battery voltage; and The processor is configured to determine an estimate of the battery capacity during a third stage of use based on the battery voltage.

32. The external device according to claim 31, wherein: Cumulative battery energy consumption is battery energy consumption data received from the implantable device.

33. The external device according to claim 31, wherein: The cumulative battery energy consumption is determined by the external device based on cumulative battery energy data received from the implantable device, the cumulative battery energy data comprising a data set of one or more values.

34. The external device according to claim 31, wherein In the intermediate stage, the battery capacity estimation value is determined by a combination of a first battery capacity estimation result based on the battery energy consumption data and a second battery estimation result based on voltage.

35. The external device according to claim 34, wherein: The first and second battery capacity results are linearly combined.

36. The external device according to claim 34, wherein: In the intermediate stage, the first and second battery capacity estimation results are linearly combined so that the first battery capacity result based on energy consumption data is fully weighted when the intermediate stage starts and the second battery capacity result based on voltage is fully weighted when the intermediate stage is completed.

37. The external device according to claim 34, wherein: The intermediate stage occurs between the battery voltage upper limit threshold and the battery voltage lower limit threshold.

38. The external device according to claim 37, wherein: When the nominal voltage of the battery is 3.1V, the battery voltage upper threshold is approximately 2.974V, and the battery voltage lower threshold is 2.870V.

39. The external device according to claim 31, wherein: In the third stage, when the voltage is lower than a battery voltage lower threshold, the estimated value of the battery capacity is based on the battery voltage.

40. The external device according to claim 39, wherein: In the third stage, the estimate of the battery capacity is based on a polynomial function of the battery voltage derived from battery characteristics.

41. The external device according to claim 39, wherein: The battery voltage lower limit threshold is between 85% and 95% of the battery nominal voltage of the battery. When the battery nominal voltage is about 3.1V, the battery voltage lower limit threshold is between 2.8V and 2.9V.

42. The external device according to claim 31, wherein In the initial stage, the estimated value of the battery capacity includes subtracting the accumulated energy discharged from the battery from the battery capacity at the beginning of battery use.

43. The external device according to claim 31, wherein: The initial stage includes a first sub-stage when the estimated value is higher than the battery capacity upper limit threshold and a second sub-stage when the estimated value is lower than the battery capacity upper limit threshold, and wherein, in the first sub-stage, the estimated value of the battery capacity is determined based on the battery energy consumption data relative to the total capacity; Therein, in the second sub-stage, the estimated value of the battery capacity is determined based on the battery energy consumption data relative to the total capacity and has a minimum value set at a battery capacity lower limit threshold.

44. The external device according to claim 43, wherein: The battery capacity upper limit threshold is approximately 75% of the battery capacity at the beginning of use, and the battery capacity lower limit threshold is approximately 46% of the battery capacity at the beginning of use.

45. The external device according to claim 43, wherein In the second sub-phase, the voltage is greater than the battery voltage upper limit threshold.

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

47. The external device according to claim 31, wherein: The battery energy consumption data includes: an active battery energy consumption dataset associated with stimulation pulse delivery; and A dataset of fixed battery energy usage associated with fixed energy consumption.

48. The external device according to claim 47, wherein: The fixed battery energy usage dataset includes battery energy usage related to at least one or any combination of the following: Fixed battery discharge; periodic communication with said external device; Housekeeping tasks associated with the execution of software within the implantable device; and The quiescent current consumed by the implantable device.

49. A method of determining an estimate of battery capacity of a battery of an implantable device during use, the method comprising: establishing communication with the implantable device via an external device; receiving information including battery energy consumption data and battery voltage from the implantable device; as well as An estimate of the battery capacity is determined based on the received information, wherein: At an initial stage of the usage period, the estimated value of the battery capacity is determined based on the battery energy consumption data relative to the battery capacity at the beginning of the usage period; as well as At a later stage during use, the estimate of the battery capacity is based on the battery voltage.

50. The method of claim 49, wherein: The latter stage is the final stage during use.

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

Images