Electrogram Fractionation Measurement With Second-Derivative Thresholds
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Solution Overview
Problem
Existing technologies fail to effectively measure features of the electrogram depolarization wave, such as local activation time, voltage amplitude, and fractionation, which are crucial for assessing cardiac tissue health.
Innovation Solution
A method and system that utilize processors to determine a noise threshold in electrograms, identify extrema, and generate a fractionation metric by counting significant excursions in the second derivative of the electrogram, enabling precise quantification of cardiac tissue condition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing technologies are used to measure EGM features, then the measurement process is simple, but the measurement precision is insufficient
Solution Approach 1:
The patent segments the EGM signal processing into distinct stages: noise threshold determination using the second derivative, extremum detection, and fractionation metric calculation. This segmentation allows each component to be optimized independently, improving overall measurement precision without requiring a completely complex system redesign.
Solution Approach 2:
The patent introduces the second derivative as an intermediary tool to determine noise thresholds and identify significant excursions. This intermediary mathematical transformation enables precise discrimination between noise and true signal features, enhancing measurement precision while maintaining algorithmic simplicity.
2Measurement precision
If noise threshold is determined using second derivative, then measurement precision improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by calculating the second derivative and determining the noise threshold before actually detecting extrema and computing the fractionation metric. This preliminary processing establishes a reference framework that simplifies subsequent extremum detection, making the overall computational burden manageable while achieving high precision.
Solution Approach 2:
The patent changes the parameter domain by transforming the EGM signal into its second derivative, which enhances the visibility of noise characteristics and extremum features. This parameter transformation enables more precise threshold-based filtering without requiring complex adaptive algorithms.
3Measurement precision
If extrema counting method is used, then measurement precision for fractionation improves, but reliability decreases due to noise sensitivity
Solution Approach 1:
The patent implements a feedback mechanism where the noise threshold, determined from the second derivative characteristics, is used to filter and validate detected extrema. This feedback loop ensures that only truly significant excursions above the noise level are counted, maintaining both precision and reliability by continuously referencing the noise characteristics.
Solution Approach 2:
The second derivative serves as an intermediary filter that mediates between the raw EGM signal and the final fractionation count. It provides a noise-aware threshold that dynamically adapts to signal conditions, preventing noise-induced false positives while maintaining sensitivity to true physiological features.
Data Source
AI summary
A method for quantifying a metric indicative of a condition of cardiac tissue in an electrogram is disclosed. The method includes detecting extrema in the electrogram, analyzing the detected extrema, selecting certain extrema based on a threshold and generating a fractionation metric including a count of the selected extrema.