Cardiac Electrogram Fractionation Metric Calculation
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Solution Overview
Problem
Current methods for quantifying and characterizing cardiac tissue health through electrograms (EGMs) lack effective measures for determining features like local activation time, voltage amplitude, and fractionation, which are crucial for distinguishing normal and abnormal tissue.
Innovation Solution
A system and method that determine a metric indicative of fractionation in EGMs by calculating local activation time, second derivatives, noise thresholds, and excursion magnitudes to quantify the degree of fractionation, enabling characterization of cardiac tissue health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional EGM analysis methods are used, then the measurement process is simple, but the measurement precision of fractionation and tissue characterization is insufficient
Solution Approach 1:
The patent segments the EGM signal analysis into multiple distinct processing stages: noise threshold determination using pre-QRS intervals, QRS onset/detection identification, fractionation metric calculation, and tissue characterization. This segmentation allows each stage to be optimized independently, improving overall measurement precision while maintaining manageable system complexity.
Solution Approach 2:
The patent performs preliminary actions by first determining noise thresholds using pre-QRS intervals before analyzing the actual depolarization wave. This preliminary noise characterization enables more accurate fractionation measurement by establishing baseline criteria for distinguishing signal from noise, thereby improving measurement precision without proportionally increasing complexity.
2Loss of information
If quantitative measurements of depolarization wave features are implemented, then tissue health characterization improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent implements feedback mechanisms where measured EGM features (amplitude, duration, fractionation metrics) are fed back into the analysis system to refine tissue characterization. This feedback loop allows the system to iteratively improve its measurements and adjust analysis parameters, reducing information loss about tissue health while managing measurement complexity through adaptive processing.
Solution Approach 2:
The patent employs parameter changes by analyzing multiple EGM features (amplitude, duration, morphology, fractionation metrics) and using these varying parameters to characterize tissue health. By measuring the same signal across different parameters, the system captures comprehensive tissue information while using systematic measurement approaches to manage the complexity of multi-parameter analysis.
3Reliability
If local activation time and fractionation metrics are calculated, then cardiac tissue abnormality detection improves, but the extent of automation required increases
Solution Approach 1:
The patent implements self-service automation where the system automatically performs noise threshold determination, QRS detection, fractionation metric calculation, and tissue characterization without requiring manual intervention. The automated processing pipeline reliably calculates local activation time and fractionation metrics by autonomously navigating the complex measurement steps, thereby improving reliability while making the extent of automation manageable through systematic design.
Data Source
AI summary
A method for quantifying a metric indicative of a condition of cardiac tissue in an electrogram is disclosed. The method comprises detecting extrema in the electrogram, analyzing the detected extrema, selecting certain extrema based on a threshold and generating a fractionation metric comprising a count of the selected extrema.