Automatic Detection Parameter Identification for Implantable Medical Devices
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Solution Overview
Problem
Existing systems for implantable medical devices struggle to intuitively specify detection parameters for identifying physiological data features, particularly in electrographic activity, making it challenging for physicians to effectively use these systems.
Innovation Solution
The development of methods and systems that allow physicians to select regions of interest in physiological data graphically, with the system automatically deriving and adjusting detection parameters for tools like half wave detectors, line length detectors, and area detectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If detection parameters are manually specified by physicians, then measurement precision can be maintained, but ease of operation deteriorates due to the non-intuitive nature of parameter specification
Solution Approach 1:
The system performs automatic parameter identification where the implantable medical device itself identifies and sets the detection parameters based on the sensed physiological data, eliminating the need for manual physician specification while maintaining detection accuracy through algorithmic analysis of the actual patient data
Solution Approach 2:
The system dynamically adjusts detection parameters based on the characteristics of the physiological data being monitored, allowing the parameters to change automatically according to the specific patient's data patterns rather than using fixed manual settings
2Ease of operation
If automatic parameter identification is implemented, then ease of operation improves, but device complexity increases due to additional algorithms and processing requirements
Solution Approach 1:
The physiological data processing system serves multiple functions: it monitors patient conditions, identifies detection parameters, and adjusts detection settings all through a single integrated automatic identification process, reducing the need for separate manual configuration systems
Solution Approach 2:
The device automatically identifies and configures its own detection parameters without requiring external programming equipment or physician intervention, making the system self-sufficient and reducing the complexity of external configuration systems
3Productivity
If manual parameter specification is used, then device complexity remains low, but loss of time increases due to the time required for physicians to understand and set parameters
Solution Approach 1:
The system performs preliminary automatic identification and configuration of detection parameters during initial system setup or data review periods, so that when clinical decisions need to be made, the parameters are already optimized and ready for immediate use
Solution Approach 2:
The system continuously monitors the effectiveness of detection parameters and automatically adjusts them based on feedback from the physiological data, improving detection accuracy over time without requiring physician reconfiguration
Data Source
AI summary
An initial set of parameters for operating one or more detection tools is automatically derived and subsequently adjusted so that each detection tool is more or less sensitive to signal characteristics in a region of interest. Detection tool(s) may be applied to physiological signals sensed from a patient (such as EEG signals) and may be configured to run in an implanted medical device that is programmable with the parameters to look for rhythmic activity, spiking, and power changes in the sensed signals, etc. A detection tool may be selected and parameter values derived in a logical sequence and/or in pairs based on a graphical representation of an activity type which may be selected by a user, for example, by clicking and dragging on the graphic via a GUI. Displayed simulations allow a user to assess what will be detected with a derived parameter set and then to adjust the sensitivity of the set or start over as desired.


