Sensing Vector Evaluation for Cardiac Signal Accuracy
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Solution Overview
Problem
Current implantable medical devices (IMDs) face challenges in efficiently evaluating and selecting optimal sensing vectors for cardiac electrical activity, leading to issues with under-sensing or over-sensing, which can result in ineffective therapy delivery and potential proarrhythmic effects.
Innovation Solution
A system and method for evaluating multiple candidate sensing vectors by generating signal intensity and interference indicators, ranking them based on these indicators, and selecting the most suitable vectors for cardiac electrical activity sensing, incorporating electrode information for improved signal quality and reduced interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple candidate sensing vectors are evaluated to improve sensing accuracy, then the reliability of cardiac signal sensing is improved, but the device complexity and time required for vector selection increases
Solution Approach 1:
The system performs preliminary evaluation of multiple candidate sensing vectors by computing signal quality metrics (such as signal-to-noise ratio, signal amplitude, and interference levels) for each vector before final selection. This preliminary assessment allows the device to pre-determine the optimal sensing vector based on quantitative criteria, improving sensing reliability while automating the selection process to minimize manual intervention and complexity.
2Reliability
If multiple candidate sensing vectors are evaluated to reduce under-sensing or over-sensing, then the reliability of therapy delivery is improved, but the time required for vector assessment increases
Solution Approach 1:
The system changes the parameters of sensing vectors by evaluating multiple candidate vectors with different electrode combinations and configurations. By systematically varying vector parameters (electrode pairs, orientations, and locations) and assessing their performance using computed metrics, the device identifies the optimal vector that minimizes under-sensing and over-sensing events, thereby improving therapy delivery reliability without requiring excessive assessment time.
3Measurement precision
If signal intensity and interference indicators are computed for each candidate vector, then the measurement precision of sensing vector evaluation is improved, but the use of energy and computational resources increases
Solution Approach 1:
The system replaces complex manual evaluation mechanisms with automated computational algorithms that calculate signal quality indicators (such as signal-to-noise ratio, signal amplitude, and interference levels) for each candidate sensing vector. This substitution of mechanical/manual assessment with electronic computation enables precise evaluation of multiple vectors while optimizing energy consumption through efficient algorithms and selective computation of metrics only for candidate vectors being considered.
Data Source
AI summary
Systems and methods for evaluating multiple candidate sensing vectors for use in sensing electrical activity of a heart are disclosed. The system can sense physiologic signals using each of a plurality of candidate sensing vectors, and generate respective signal intensity indicators and interference indicators using the physiologic signals sensed by using the respective sensing vectors. The system can also receive electrode information of each of the candidate sensing vectors, including information about sensing electrodes that are also used for delivering cardiac electrostimulation. The system can rank at least some of the plurality of candidate sensing vectors according to the signal intensity indicators, the interference indicators, and the electrode information. The system can also include a user interface for displaying the ranked sensing vectors, and allowing the user to select at least one sensing vector for use in sensing the cardiac electrical activity.