Implantable Device Automatic Sensing Configuration Selection
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Solution Overview
Problem
Implantable cardiac stimulation devices face challenges in accurately sensing cardiac activity due to electrode configurations, leading to potential double counting of cardiac events, which can result in unnecessary shocks, and existing methods require clinical intervention for configuration adjustments.
Innovation Solution
An implantable medical device with multiple sensing electrodes and a controller that evaluates signals from different configurations to automatically select the configuration with the lowest risk of double counting, allowing for improved sensing performance without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If true bipolar sensing configuration is used, then sensing localization is improved, but double counting of cardiac events increases
Solution Approach 1:
The system dynamically switches between true bipolar and integrated bipolar sensing configurations based on real-time evaluation of signal quality and double-counting detection. The controller automatically selects the optimal configuration without requiring manual intervention, adapting the sensing mode to current physiological conditions.
Solution Approach 2:
The system changes the sensing configuration parameter by switching between true bipolar and integrated bipolar modes. This parameter change allows the system to optimize the balance between sensing localization precision and reliability by selecting the configuration that minimizes double-counting errors while maintaining accurate cardiac event detection.
2Reliability
If integrated bipolar sensing configuration is used, then double counting is reduced, but P wave sensing interference increases
Solution Approach 1:
The system dynamically evaluates both sensing configurations and automatically switches between them based on which configuration provides better overall performance for the current physiological conditions. The controller monitors for both double-counting errors and P-wave sensing accuracy, selecting the configuration that optimizes both parameters.
3Measurement precision
If manual configuration evaluation is performed, then sensing optimization is achieved, but clinical intervention time is required
Solution Approach 1:
The system performs self-evaluation of multiple sensing configurations and automatically selects the optimal configuration without requiring clinician intervention. The controller continuously monitors signal quality metrics and double-counting events, autonomously adjusting the sensing configuration to maintain optimal performance.
Solution Approach 2:
The system performs preliminary evaluation of sensing configurations during implantation and continues to evaluate and adjust configurations over time. By proactively assessing multiple configurations and automatically selecting the best one, the system eliminates the need for subsequent clinical follow-up visits specifically for configuration optimization.
4Measurement precision
If multiple sensing configurations are evaluated, then sensing accuracy is improved, but device complexity increases
Solution Approach 1:
The controller is designed to perform multiple functions: it can operate in both true bipolar and integrated bipolar sensing modes, evaluate signal quality metrics, detect double-counting events, and automatically switch between configurations. This multi-functionality allows the system to maintain simplicity while achieving improved sensing accuracy through automatic configuration selection.
Data Source
AI summary
An implantable medical device system that senses physiologic processes via multiple sensor signal configurations. The device can further process the sensor configurations to obtain additional processed signal configurations. The device can utilize the processed configurations for ongoing sensing of the physiologic process. The device can also automatically evaluate the multiple sensor configurations as well as the processed configurations and select the configuration offering the best signal discrimination to reduce oversensing or erroneously interpreting secondary characteristics of the physiologic process as corresponding to primary characteristics of the process as in double-counting. The signal discrimination can be evaluated as an absolute margin and/or a ratio between amplitudes of the primary and secondary characteristics. The signal discrimination can also be evaluated based at least in part on a calculated mean and standard deviation according to each configuration.


