Cardiac Phenomena Prevalence Detection via Discrete EP Data Segmentation
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Solution Overview
Problem
Existing systems for detecting cardiac phenomena based on electrophysiological data do not accurately determine the prevalence of these phenomena, leading to inadequate identification of arrhythmia sources during diagnostic and therapeutic procedures.
Innovation Solution
A system and method that utilize sensors on medical devices to detect cardiac phenomena at multiple locations and times, determining prevalence by analyzing electrophysiological data and displaying this information to clinicians, enabling precise identification of arrhythmia sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional point-by-point recording methods are used to map electrogram data, then the system can identify arrhythmia regions, but the measurement precision and reliability of detecting cardiac phenomenon prevalence is insufficient
Solution Approach 1:
The patent segments the detection process into multiple discrete time points and locations, collecting electrogram data systematically across the cardiac tissue surface. By dividing the measurement into discrete segments (time points and locations), the system achieves more precise prevalence detection while maintaining reliable arrhythmia source identification through comprehensive coverage.
Solution Approach 2:
The system implements feedback by comparing detected cardiac phenomena across multiple time points and locations, using the prevalence information to refine and confirm arrhythmia source identification. This feedback mechanism enhances both measurement precision and reliability by validating detections through repeated measurements and cross-referencing multiple data points.
2Measurement precision
If data is collected at multiple locations and times, then the prevalence information becomes more accurate, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent employs a multi-functional system where the electrophysiology mapping system performs multiple functions: data acquisition at multiple locations, temporal analysis across time points, prevalence calculation, and arrhythmia source identification. This universal approach consolidates what could be separate complex systems into one integrated platform, achieving high measurement precision without proportionally increasing overall device complexity.
Solution Approach 2:
The system manages complexity by systematically varying parameters (time points, locations) according to defined protocols, then processing the resulting data through standardized prevalence calculations. This parameter-based approach allows comprehensive multi-location, multi-time data collection while maintaining manageable system complexity through structured data organization and processing algorithms.
Data Source
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AI summary
Systems and methods for determining prevalence of a cardiac phenomenon based on electrophysiological (EP) data from a tissue of a body are provided. The EP data is measured by at least one sensor disposed on at least one medical device that is positionable near the tissue of the body. A system includes an electronic control unit communicatively coupled to a display device and configured to, for each of the plurality of locations, detect, at each of a plurality of discrete times occurring during a predetermined time period, whether a cardiac phenomenon occurs at the location based on the EP data, determine a prevalence of the cardiac phenomenon based on the detecting, and display information indicative of the determined prevalence of the cardiac phenomenon on the display device.