Sensing Vector Selection Using Polynomial Analysis
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Solution Overview
Problem
Implantable cardiac stimulus devices face challenges in efficiently selecting optimal sensing vectors for cardiac event detection, which can be time-consuming and prone to human error, especially as patient physiology changes over time.
Innovation Solution
The implementation of methods and devices that analyze and select suitable sensing vectors using mathematical formulas and scoring systems to determine vector suitability for cardiac event detection, allowing for automatic selection and adaptation to changing patient conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual vector selection methods are used, then device complexity is reduced, but measurement precision and reliability of cardiac event detection deteriorate
Solution Approach 1:
The system automatically analyzes and selects optimal sensing vectors without requiring manual intervention. The microprocessor executes algorithms that evaluate multiple vectors and autonomously determines the best vector(s) for cardiac event detection, eliminating the need for physician manual selection while maintaining high accuracy.
Solution Approach 2:
The system transforms the vector selection process from a manual qualitative assessment to an automated quantitative analysis. By changing parameters such as signal amplitude, noise ratio, and vector stability into measurable numerical values, the system enables objective automated selection based on mathematical criteria rather than subjective manual judgment.
2Reliability
If comprehensive vector analysis is performed, then reliability of detection improves, but time required for vector selection increases
Solution Approach 1:
The system performs preliminary analysis of multiple sensing vectors during an initialization phase before actual cardiac event detection begins. By pre-evaluating vector quality metrics and storing optimal vector information, the system ensures reliable detection is ready to operate immediately without time-consuming analysis during critical monitoring periods.
Solution Approach 2:
The microprocessor continuously monitors and updates vector quality metrics in the background during device operation. This continuous analysis allows the system to maintain optimal vector selection without interrupting cardiac monitoring, ensuring both high reliability and minimal time loss through parallel processing of vector evaluation and event detection.
3Productivity
If automated vector selection is implemented, then productivity of detection system improves, but device complexity increases
Solution Approach 1:
The system automatically analyzes and selects optimal sensing vectors without requiring manual intervention. The microprocessor executes algorithms that evaluate multiple vectors and autonomously determines the best vector(s) for cardiac event detection, eliminating the need for physician manual selection while maintaining high accuracy.
Solution Approach 2:
The patent replaces manual mechanical vector selection with automated computational algorithms. By substituting human expertise with programmed mathematical models and processing algorithms, the system achieves consistent automated vector analysis that can process multiple vectors simultaneously, improving productivity while managing complexity through software rather than hardware complexity.
Data Source
AI summary
Methods and devices configured for analyzing sensing vectors in an implantable cardiac stimulus system. In an illustrative example, a first sensing vector is analyzed to determine whether it is suitable, within given threshold conditions, for use in cardiac event detection and analysis. If so, the first sensing vector may be selected for detection and analysis. Otherwise, and in other examples, one or more additional sensing vectors are analyzed. A polynomial may be used during analysis to generate a metric indicating the suitability of the sensing vector for use in cardiac event detection and analysis. Additional illustrative examples include systems and devices adapted to perform at least these methods, including implantable medical devices, and/or programmers for implantable medical devices, and/or systems having both programmers and implantable medical devices that cooperatively analyze sensing vectors.