Adaptive End-of-Life Forecasting for Rechargeable Implantable Medical Devices
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Solution Overview
Problem
Existing implantable medical devices with rechargeable batteries face challenges in determining the end-of-life (EOL) of the battery, as the current shutdown time is fixed and does not account for varying usage patterns, leading to premature device exhaustion for strenuous users and potential continued use by light users beyond the intended lifespan.
Innovation Solution
A circuitry and algorithm that estimates the battery capacity based on historical parameters such as charging and usage data, allowing for the forecasting and determination of the EOL, enabling the suspension or continuation of therapeutic operations and adjusting the shutdown time accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed shutdown time is used for the implantable medical device, then the device can be manufactured with a predetermined lifespan, but the device may exhaust prematurely for strenuous users or continue use beyond intended lifespan for light users
Solution Approach 1:
The patent implements a dynamic end-of-life determination system that transitions from a fixed shutdown time to a flexible, usage-adaptive EOL forecast. The system continuously monitors battery parameters and usage patterns, updating the EOL prediction dynamically based on actual device performance and consumption rates, allowing the shutdown time to adapt to individual user needs
Solution Approach 2:
The patent employs feedback mechanisms by continuously monitoring battery voltage, current, temperature, and usage patterns, then using this data to refine EOL predictions. The system provides feedback to both the control algorithm and the user, enabling real-time adjustment of the shutdown timing based on actual battery degradation and usage intensity
2Duration of action of moving object
If the shutdown time is extended for light users, then device lifespan is maximized, but the battery may be depleted before the intended shutdown time
Solution Approach 1:
The patent implements preliminary action by forecasting the end-of-life before it actually occurs. The system uses historical usage data and battery degradation models to predict future battery capacity, allowing the device to plan shutdown timing in advance. This proactive approach ensures the device shuts down before complete depletion while maximizing usable lifespan
Solution Approach 2:
The patent utilizes parameter changes by monitoring multiple battery parameters (voltage, current, temperature, capacity) and using their variations over time to predict EOL. The system tracks changes in these parameters to detect degradation trends and adjust the shutdown prediction accordingly, maintaining reliability while extending lifespan
3Reliability
If the shutdown time is shortened for strenuous users, then the battery is prevented from depleting, but the device life is reduced prematurely
Solution Approach 1:
The system uses real-time feedback from usage monitoring to adjust EOL predictions for strenuous users. By detecting high current draws, frequent recharging, and intense usage patterns, the algorithm dynamically shortens the forecasted EOL to prevent battery depletion while avoiding premature shutdown through continuous refinement of the prediction based on actual performance
4Adaptability or versatility
If a fixed shutdown time is implemented, then manufacturer obligations are clearly defined, but individual user needs are not accommodated
Solution Approach 1:
The patent applies preliminary action by collecting and analyzing usage data throughout the device lifecycle to forecast EOL before battery depletion occurs. The system accumulates information about usage patterns, recharging frequency, and battery performance, then uses this data to predict the optimal shutdown timing, preserving information rather than losing it
Data Source
AI summary
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.