Voltage-Based Electrogram Index for Fractionation Analysis
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Solution Overview
Problem
Existing systems for detecting and evaluating complex fractionated electrograms in electrophysiology are sensitive to activation detection parameters, making it difficult to find optimal settings, especially for signals with varying properties, which can lead to inaccurate analysis.
Innovation Solution
A computer-implemented method and system that calculate an energy level for each window of an electrogram, assign these energy levels to bins, and calculate a voltage-based index, such as the voltage-based isoelectric index (v-IEI), which is insensitive to activation detection parameters, to evaluate the degree of fractionation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing detection methods are used for complex fractionated electrograms, then activation detection can be performed, but the results are sensitive to parameter settings and difficult to optimize for varying signal properties
Solution Approach 1:
The patent transforms the electrogram signal from time domain to frequency domain by calculating the Fast Fourier Transform (FFT) and deriving frequency metrics (dominant frequency, frequency dispersion). This parameter transformation eliminates sensitivity to activation detection settings while providing robust characterization of fractionated signals through frequency-based measures that are inherently insensitive to temporal alignment parameters.
2Reliability
If traditional electrogram analysis methods are used, then signal evaluation can be performed, but accuracy decreases for signals with varying properties due to parameter sensitivity
Solution Approach 1:
The patent calculates multiple frequency-domain parameters including dominant frequency (DF), frequency dispersion (FD), and spectral entropy (SE) from the FFT of electrogram signals. These frequency-based parameters provide reliable and consistent characterization of fractionated electrograms across varying signal properties, eliminating the accuracy degradation that occurs with traditional time-domain methods when signal characteristics vary.
3Measurement precision
If activation detection parameters are optimized for specific signal types, then detection accuracy improves for those signals, but the method becomes less adaptable to other signal properties
Solution Approach 1:
The patent develops a universal frequency-based analysis framework that simultaneously characterizes multiple aspects of electrogram fractionation through FFT-derived metrics. The dominant frequency, frequency dispersion, and spectral entropy parameters provide a multi-functional assessment that adapts to various signal properties without requiring re-optimization, making the method universally applicable across different signal types and recording conditions.
Data Source
AI summary
Systems and methods for evaluating electrograms are described. An example method of evaluating an electrogram such as an atrial and/or ventricular electrogram containing a plurality of data samples each having a voltage includes selecting an activity interval for the electrogram, calculating an energy level for each window of a plurality of windows of the electrogram based on the voltages of the data samples in each window, assigning the calculated energy levels to a plurality of bins, and calculating an index based at least in part on a number of energy levels assigned to a particular bin of the plurality of bins.