Cardiac Event Detection With Priority-Based Resource Allocation
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Solution Overview
Problem
Existing implantable medical devices (IMDs) face challenges with repeated false positive detections of cardiac events, leading to inappropriate therapies and resource-intensive human review, which can be costly and time-consuming.
Innovation Solution
A system and method for adjusting cardiac event detection settings using supervised learning from patient prior physiologic event episodes, involving user adjudication and comparison of detection settings to reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detection sensitivity is increased to improve true positive recognition, then more cardiac events are detected, but false positive detections increase
Solution Approach 1:
The system performs preliminary actions by analyzing stored physiologic event episodes before final detection. Historical data is reviewed and simulated under different detection settings to predict future detection accuracy, allowing the system to prepare and optimize detection parameters in advance rather than reacting to false positives after they occur
Solution Approach 2:
The system implements feedback mechanisms where detection performance is continuously monitored and used to adjust future detection settings. User adjudication of detected events provides feedback that feeds back into the detection algorithm optimization, creating a closed-loop system that learns from past performance to reduce future false positives while maintaining sensitivity
2Measurement precision
If human review of physiologic event episodes is increased to reduce false positives, then detection accuracy improves, but clinical resources and time are consumed
Solution Approach 1:
The system performs self-service by automatically analyzing and simulating detection performance using stored historical data. The system self-evaluates its detection accuracy across different settings without requiring continuous human intervention, generating its own optimization recommendations based on simulated performance metrics
Solution Approach 2:
The patent replaces the mechanical system of manual human review with an automated computational simulation system. Instead of clinicians manually reviewing each episode, the system uses computer-based simulations to evaluate detection performance across multiple scenarios, substituting human cognitive processing with automated algorithmic analysis
3Object-generated harmful factors
If detection settings are frequently adjusted to address false positives, then false positive rate decreases, but device complexity and operational complexity increase
Solution Approach 1:
The system dynamically adjusts detection settings based on simulated performance data and historical patterns. Rather than static fixed settings, the detection parameters adapt over time based on learned patterns from patient data, allowing the system to respond to changing conditions automatically without manual reconfiguration
Solution Approach 2:
The system manages complexity by systematically varying detection parameters through simulation to identify optimal settings. Multiple detection settings are simulated and compared, allowing the system to navigate the parameter space efficiently and select settings that minimize false positives while maintaining sensitivity, reducing the need for manual parameter tuning
Data Source
AI summary
Systems and methods for detecting cardiac events in a patient are described herein. An embodiment of a medical system includes a detection circuit configured to detect cardiac events of the patient at different detection settings using physiologic information of the patient and a control circuit to determine a priority for each detected cardiac event based on the detection setting used to detect the respective cardiac event and to prioritize system resources for the detected cardiac event according to the determined priorities, wherein to prioritize system resources comprises to allocate memory space for the detected cardiac events or to prioritize data transmission of the detected cardiac events to an external system based on the determined priority for each respective cardiac event.