Cycle Length Iteration for Atrial Fibrillation Electrogram Detection
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Solution Overview
Problem
Current methods for detecting atrial fibrillation activation rates in electrograms are plagued by complexity, leading to inaccurate measurements due to variability in cycle lengths and morphologies, with existing algorithms prone to oversensing or undersensing, and lack robustness for clinical validation.
Innovation Solution
The Cycle Length Iteration (CLI) algorithm iteratively adjusts detection thresholds to converge mean and median cycle lengths, providing a more accurate and robust method for detecting atrial fibrillation activations and calculating cycle lengths, which is computationally efficient and suitable for real-time applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual threshold setting is used to detect deflections, then detection accuracy may be improved, but subjectivity and technician variability increase
Solution Approach 1:
The system performs self-calibration by automatically determining optimal threshold values through iterative testing and validation, eliminating the need for manual technician intervention. The algorithm autonomously adjusts detection parameters based on the specific electrogram characteristics, making the system self-sufficient and removing human subjectivity from the process.
Solution Approach 2:
The system dynamically changes detection parameters (threshold values, sensitivity settings) based on the characteristics of the incoming electrogram signal. Through iterative optimization, the algorithm adapts parameters to match the specific morphology and amplitude variations of each patient's atrial fibrillation electrograms, achieving high detection accuracy without manual intervention.
2Extent of automation
If fixed threshold algorithms are used for deflection detection, then automation is improved, but oversensing and undersensing errors increase
Solution Approach 1:
The system transitions from static fixed thresholds to dynamic adaptive thresholds that automatically adjust based on signal characteristics. The algorithm continuously monitors electrogram morphology and amplitude variations, dynamically modifying detection parameters to maintain high accuracy across diverse signal conditions without manual intervention.
Solution Approach 2:
The system implements feedback mechanisms where detection results are continuously evaluated and used to refine threshold settings. Through iterative validation against ground truth data, the algorithm learns from detection errors and adjusts parameters accordingly, reducing oversensing and undersensing while maintaining automation.
3Device complexity
If dominant frequency analysis is used to estimate activation rates, then computational simplicity is improved, but accuracy decreases for irregular waveforms
Solution Approach 1:
The system segments the electrogram signal into individual deflection events rather than analyzing the overall frequency spectrum. By detecting and timing discrete activation events, the algorithm directly calculates activation rates without relying on frequency domain transformations, maintaining accuracy for irregular waveforms while keeping computational requirements manageable.
Solution Approach 2:
The system replaces the frequency-domain mechanical approach (Fourier transform-based dominant frequency analysis) with a time-domain event detection approach. By directly detecting deflection events and measuring intervals between them, the algorithm achieves superior accuracy for irregular atrial fibrillation waveforms while maintaining computational efficiency through straightforward time-based calculations.
4Measurement precision
If manual measurement of deflection intervals is used, then measurement accuracy is improved, but time consumption and labor intensity increase
Solution Approach 1:
The system performs automatic detection and measurement of deflection intervals without requiring manual technician intervention. The algorithm autonomously identifies deflections, calculates cycle lengths, and generates activation rate maps, eliminating the labor-intensive manual measurement process while maintaining or improving accuracy through consistent automated application of detection criteria.
Solution Approach 2:
The system replaces manual mechanical measurement (using calipers on printed electrograms) with automated computer-based detection and calculation. The algorithm processes digital electrogram signals to automatically determine deflection timing and interval measurements, dramatically increasing productivity while maintaining accuracy through precise digital signal processing.
Data Source
AI summary
A method is provided of analyzing a cardiac electrogram using a computer. In one step, a determination is made as to a plurality of cycle lengths between a plurality of activation peaks of the cardiac electrogram. In another step, a determination is made as to whether a mean of the plurality of cycle lengths meets at least one criteria. The method may be used to iteratively adjust a detection threshold level for detecting atrial fibrillation based on the cardiac electrogram.