Event Triggered Prognostics for Implantable Medical Devices
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Solution Overview
Problem
Conventional implantable medical devices (IMDs) face challenges in detecting intermittent and potentially adverse device conditions due to power constraints, which limit their ability for continuous real-time monitoring and timely identification of faults, leading to potential errors and adverse performance.
Innovation Solution
A system that includes a medical electrical lead coupled to an IMD with a control module that derives and evaluates parameters to detect leading indicators and system-critical indicators, activating diagnostic evaluations in real-time to identify potential faults before they cause significant issues, using a combination of physiological signals and therapy delivery monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous real-time monitoring is implemented to detect device conditions, then reliability is improved, but power consumption increases
Solution Approach 1:
The patent implements periodic monitoring at different intervals based on device state. Routine prognostic sensors take measurements at scheduled intervals rather than continuously, while critical sensors activate at higher frequencies when anomalies are detected. This periodic action reduces average power consumption while maintaining reliability through strategic sampling at key moments in the device operational cycle.
Solution Approach 2:
The system uses existing device operational data and routine sensor measurements to self-diagnose device conditions. The prognostic analysis leverages data already being collected for therapy delivery and monitoring, eliminating the need for dedicated continuous monitoring power consumption. The device monitors itself using its own operational parameters without requiring additional active monitoring resources.
2Measurement precision
If computationally expensive firmware algorithms are used for prognostic analysis, then measurement precision is improved, but power consumption increases
Solution Approach 1:
The prognostic analysis is segmented into multiple levels: routine analysis using simple threshold comparisons on operational parameters, and advanced analysis using computationally intensive algorithms only when anomalies are detected. This segmentation allows the system to maintain high measurement precision for critical conditions while consuming minimal power during normal operation by using simpler evaluation methods.
Solution Approach 2:
The system applies computationally expensive prognostic algorithms partially—only when triggered by specific conditions or anomalies rather than continuously. During normal operation, simpler monitoring methods are used. This partial application of advanced algorithms maintains measurement precision for critical detection while significantly reducing overall power consumption compared to continuous expensive analysis.
3Device complexity
If periodic testing is used for fault detection, then device complexity is reduced, but reliability worsens due to intermittent condition detection
Solution Approach 1:
The system performs preliminary prognostic assessments using routine sensor data and operational parameters before critical failures occur. By analyzing trends in lead impedance, battery voltage, and therapy delivery parameters over time, the system detects early signs of degradation and triggers more comprehensive diagnostics before intermittent conditions develop into failures, thereby improving reliability without significantly increasing complexity.
Solution Approach 2:
The monitoring system incorporates feedback loops where routine measurement results trigger adjusted monitoring frequencies and thresholds. When abnormal patterns are detected, the system increases monitoring intensity and activates additional diagnostic routines. This feedback mechanism allows the relatively simple periodic testing framework to adapt dynamically, improving detection of intermittent conditions while maintaining overall system simplicity.
Data Source
AI summary
The present disclosure describes an implantable medical device utilizing an event-triggered prognostic indicator. The disclosure describes techniques for prognostics and management of implantable medical systems to facilitate continuity of performance of sensing and therapy delivery functions by providing adequate response time to handle emerging issues prior to adverse clinical impacts. In accordance with the present disclosure, event-triggered prognostic indicators facilitate the identification of potential device conditions.