Determining and forecasting end of life for an implantable medical device having a rechargeable battery

• US 9,446,244 B2
• Filed: 01/09/2015
• Issued: 09/20/2016
• Est. Priority Date: 01/16/2014
• Status: Active Grant

• Current Assignee: Boston Scientific Neuromodulation Corporation (Boston Scientific Corporation)
• Original Assignee: Boston Scientific Neuromodulation Corporation (Boston Scientific Corporation)
• Inventors: Marnfeldt, Goran N., Carbunaru, Rafael, Parramon, Jordi
• Primary Examiner(s): Wieland, Robert N
• Application Number:
  US14/593,742
• Publication Number:
  US 20150196765A1
• Time in Patent Office: 620 Days
• US Class Current: 1/1
• CPC Class Codes: A61N 1/36128 Control systems
• CPC Class Codes: A61N 1/3708 for power depletion
• CPC Class Codes: A61N 1/378 Electrical supply
• CPC Class Codes: G01R 31/3648 comprising digital calculation means, e.g. for performing an algorithm
• CPC Class Codes: G01R 31/392 Determining battery ageing or deterioration, e.g. state of health
• CPC Class Codes: H02J 2310/23 The load being a medical device, a medical implant, or a life supporting device
• CPC Class Codes: H02J 7/00 Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
• CPC Class Codes: H02J 7/00034 Charger exchanging data with an electronic device, i.e. telephone, whose internal battery is under charge
• CPC Class Codes: H02J 7/0048 Detection of remaining charge capacity or state of charge [SOC]

Abstract

An algorithm programmed into the control circuitry of a rechargeable-battery Implantable Medical Device (IMD) is disclosed that can quantitatively forecast and determine the timing of an early replacement indicator (tEOLi) and an IMD End of Life (tEOL). These forecasts and determinations of tEOLi and tEOL occur in accordance with one or more parameters having an effect on rechargeable battery capacity, such as number of charging cycles, charging current, discharge depth, load current, and battery calendar age. The algorithm consults such parameters as stored over the history of the operation of the IMD in a parameter log, and in conjunction with a battery capacity database reflective of the effect of these parameters on battery capacity, determines and forecasts tEOLi and tEOL Such forecasted or determined values may also be used by a shutdown algorithm to suspend therapeutic operation of the IMD.

Claims

1. Circuitry for a medical device, comprising:

a rechargeable battery;

control circuitry configured to configured to implement an algorithm, wherein the algorithm is configured to;

estimate a capacity of the battery at different points in time during operation of the medical device,

store the estimated capacities and their associated times in the medical device, and

forecast and/or determine an end of life of the medical device by deriving a function of battery capacity versus time using the stored estimated capacities and their associated times.

2. The circuitry of claim 1, wherein the algorithm forecasts and/or determines the end of life in accordance with a first capacity threshold.

3. The circuitry of claim 2, wherein the algorithm is further configured to forecast and/or determine an early indicator of the end of life.

4. The circuitry of claim 3, wherein the early indicator comprises a set time before the forecasted end of life.

5. The circuitry of claim 3, wherein the algorithm forecasts and/or determines the early indicator of the end of life in accordance with a second capacity threshold.

6. The circuitry of claim 3, wherein the algorithm is further configured to store the forecasted and/or determined early indicator for transmission to an external device.

7. The circuitry of claim 1, wherein the algorithm suspends therapeutic operation of the medical device when the end of life is determined.

8. The circuitry of claim 1, wherein the algorithm is further configured to use the forecasted end of life to extend therapeutic operation of the medical device beyond a shutdown time.

9. The circuitry of claim 1, wherein the control circuitry further comprises:

memory configured to store at least one parameter having an effect on a capacity of the rechargeable battery, wherein the at least one parameter is selected from a group consisting of one or more parameters relevant to;

previous charging of the battery, previous use of the medical device to provide therapy, and the age of the battery;

wherein the algorithm is configured to estimate the capacity of the battery at the different points in time using the at least one parameter.

10. The circuitry of claim 9, wherein the at least one parameter is stored as a function of time in the memory.

11. The circuitry of claim 9, wherein the at least one parameter is stored as a present value for use by the algorithm.

12. The circuitry of claim 9, wherein the at least one parameter comprises a value computed from at least one other parameter measured during previous charging of the battery or previous use of the medical device.

13. The circuitry of claim 9, wherein parameters relevant to previous charging of the rechargeable battery comprise a number of previous charging session, a voltage of the battery at the start of a previous charging session, a voltage of the battery at the end of a previous charging session, a duration of a previous charging session, a charge provided to the battery during a previous charging session, a discharge depth comprising a difference between a voltage of the battery at the start and finish of a previous charging session, and a battery charging current provided to the battery during a previous charging session.

14. The circuitry of claim 9, wherein parameters relevant to previous use of the medical device to provide therapy comprise a voltage of the rechargeable battery during a previous use, a load current drawn from the battery during a previous use, a power drawn from the battery during a previous use, a duration a use, and a charge drawn from the battery during a previous use.

15. The circuitry of claim 9, further comprising a battery capacity database, wherein the battery capacity database associates the at least one parameter with a change in the capacity of the battery, wherein the algorithm compares the at least one parameter to a change in the capacity in the battery capacity database to determine the capacity of the battery at the different points in time.

16. The circuitry of claim 9, wherein the memory further comprises a weight or priority of each at least one parameter, wherein the algorithm is configured to determine the capacity of the battery at the different points in time by using the weight or priority or both the weight and priority of the at least one parameter.

17. A method for operating a medical device having a rechargeable battery, comprising:

estimating and storing in the medical device a capacity of the rechargeable battery of the medical device as a function of time during operation of the medical device, and

forecasting and/or determining an end of life of the medical device by extrapolating from the capacity as a function of time a time at which the capacity equals a capacity threshold.

18. Circuitry for a medical device, comprising:

a rechargeable battery;

a first register configured to store a shutdown time at which therapeutic operation of the medical device will be suspended;

control circuitry configured to configured to implement an algorithm, wherein the algorithm is configured to;

determine an end of life of the medical device using at least a first estimated capacity of the battery; and

suspend therapeutic operation of the medical device even if the shutdown time has not been reached.

19. The circuitry of claim 18, wherein the algorithm is further configured to resume therapeutic operation of the medical device upon receipt of an override.

20. The circuitry of claim 18, wherein the algorithm is further configured to

forecast an end of life of the medical device using at least a second estimated capacity of the battery;

determine that the shutdown time has been reached;

continue therapeutic operation of the medical device if the forecasted end of life is greater than the shutdown time; and

suspend therapeutic operation of the medical device if the determined end of life is not greater than the shutdown time.

21. The circuitry of claim 20, wherein the algorithm is configured to continue therapeutic operation of the medical device by adjusting the shutdown time to a later time in the first register.

22. The circuitry of claim 21, wherein the later time is between the shutdown time and the forecasted end of life.

23. The circuitry of claim 21, wherein the later time is of a fixed duration.

24. Circuitry for a medical device, comprising:

a rechargeable battery;

a first register configured to store a shutdown time at which therapeutic operation of the medical device will be suspended;

control circuitry configured to configured to implement an algorithm, wherein the algorithm is configured to;

forecast an end of life of the medical device using at least a first estimated capacity of the battery;

determine that the shutdown time has been reached;

continue therapeutic operation of the medical device if the forecasted end of life is greater than the shutdown time; and

suspend therapeutic operation of the medical device if the determined end of life is not greater than the shutdown time.

25. The circuitry of claim 24, wherein the algorithm is configured to continue therapeutic operation of the medical device by adjusting the shutdown time to a later time in the first register.

26. The circuitry of claim 25, wherein the later time is between the shutdown time and the forecasted end of life.

27. The circuitry of claim 25, wherein the later time is of a fixed duration.