Cardiac Arrhythmia Detection Threshold Adjustment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cardiac monitoring devices are complex and overwhelming for clinicians, often producing large amounts of false positives and requiring manual selection of parameters, thresholds, and zones, which can lead to inaccurate and inefficient cardiac arrhythmia detection.
Innovation Solution
A system and method that adjusts cardiac arrhythmia detection using user-verified candidate cardiac events, where physiological information is received, displayed to the user, and adjusted based on user feedback through a user interface with selectable indicators, allowing for more accurate detection tailored to specific patient needs and clinician preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual selection of parameters, thresholds, and zones is used for cardiac event detection, then the device can be customized to specific patient needs, but the device complexity and ease of operation deteriorate due to overwhelming clinician burden
Solution Approach 1:
The system automatically adjusts detection parameters, thresholds, and zones based on user feedback about detected cardiac events. The device serves itself by learning from clinician corrections and automatically optimizing its detection algorithm without requiring manual parameter selection, thus maintaining adaptability while reducing complexity
Solution Approach 2:
The system implements a feedback loop where clinician responses to detected events (confirming or correcting detections) are used to automatically adjust detection parameters. This continuous feedback mechanism enables the device to adapt to patient needs while eliminating the burden of manual parameter selection
2Measurement precision
If sophisticated detection techniques are used to improve arrhythmia detection accuracy, then detection precision improves, but false positives increase and ease of operation worsens
Solution Approach 1:
The system uses clinician feedback on detected events to automatically adjust detection thresholds and parameters. By learning from confirmed true positives and corrected false positives, the system continuously optimizes its detection algorithm to maintain high accuracy while reducing false alarms
Solution Approach 2:
The detection algorithm self-optimizes by automatically adjusting its sensitivity and threshold parameters based on accumulated user feedback. This eliminates the need for manual tuning while reducing false positives through continuous automated calibration
3Reliability
If multiple parameters and thresholds are manually adjusted to reduce false positives, then detection reliability improves, but ease of operation and productivity deteriorate due to time-consuming adjustments
Solution Approach 1:
The system automatically performs parameter optimization and threshold adjustment based on user feedback, eliminating the need for manual adjustments. The device self-calibrates its detection algorithm to maintain high reliability without requiring time-consuming manual intervention
Solution Approach 2:
Clinician responses to detected events provide continuous feedback that automatically drives parameter optimization. The system learns from each interaction to improve detection reliability over time without requiring manual reconfiguration
4Reliability
If comprehensive cardiac monitoring is implemented to detect all potential events, then detection coverage improves, but false positives increase and ease of operation worsens
Solution Approach 1:
The system uses clinician feedback on detected events to dynamically adjust detection sensitivity and threshold parameters. This feedback-driven optimization maintains comprehensive event detection coverage while automatically filtering out false positives that burden the clinician
Data Source
Figure 1
Figure 2
Figure 3
AI summary
This document discusses, among other things, systems and methods to adjust arrhythmia detection using physiological information of a patient, including detecting a candidate cardiac event about a threshold, displaying the detected candidate cardiac event to a user, receiving user information about the detected candidate cardiac event, and adjusting an arrhythmia detection threshold based upon the received user information.