Electrogram Selection in Cardiac Mapping Using Importance Metrics
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Solution Overview
Problem
Conventional cardiac mapping systems face challenges in accurately and efficiently interpreting large volumes of electrograms (EGMs) due to their complex nature and susceptibility to electrical artifacts, leading to misleading maps and increased examination time and cost.
Innovation Solution
An electrophysiology system that assesses importance metrics for cardiac electrical signals by analyzing activation waveforms and novelty scores, using machine learning techniques to select and present significant electrograms for enhanced diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional mapping systems capture large volumes of electrograms for comprehensive cardiac mapping, then mapping completeness is improved, but examination time and cost increase significantly
Solution Approach 1:
The system extracts and selects only the most significant electrograms from the large volume of captured signals using machine learning algorithms. The machine learning model identifies and extracts electrograms with highest diagnostic value, separating them from redundant or less informative signals, thereby reducing the workload for manual inspection while maintaining mapping quality.
Solution Approach 2:
The system creates a curated subset or copy of the most important electrograms rather than requiring review of all captured signals. This selected representative sample maintains the essential diagnostic information while significantly reducing the quantity of signals needing manual examination, thus solving the time complexity issue.
2Reliability
If conventional systems manually inspect all captured electrograms for diagnostic accuracy, then diagnostic reliability is improved, but examination time and cost increase
Solution Approach 1:
The machine learning system provides feedback by automatically evaluating and ranking electrograms based on their diagnostic significance. This feedback mechanism allows the system to identify which electrograms are most likely to be diagnostically relevant, enabling selective manual review of high-value signals while maintaining diagnostic accuracy without requiring inspection of all signals.
Solution Approach 2:
The system replaces the mechanical process of manual visual inspection with an automated machine learning-based evaluation system. The machine learning algorithms automatically assess electrogram significance, substituting human manual review for computational analysis, thereby maintaining diagnostic reliability while significantly reducing examination time.
3Quantity of substance
If conventional mapping systems process all captured electrograms to construct comprehensive maps, then mapping completeness is improved, but map accuracy decreases due to electrical artifacts and complex data nature
Solution Approach 1:
The machine learning system extracts and filters out electrograms that are most likely to contain diagnostic information, separating them from signals affected by electrical artifacts or other confounding factors. This extraction process maintains mapping completeness by including all relevant signals while improving accuracy by excluding noisy or artifact-contaminated data.
Solution Approach 2:
The system changes the parameters used for electrogram evaluation by employing machine learning models that can dynamically adjust criteria for signal significance. These parameter changes allow the system to adapt to different cardiac conditions and artifact types, maintaining map accuracy while processing comprehensive datasets.
4Adaptability or versatility
If conventional systems use complex processing techniques to analyze electrograms, then diagnostic capability is improved, but system complexity and difficulty of interpretation increase
Solution Approach 1:
The system replaces complex manual analysis procedures with automated machine learning algorithms that can handle sophisticated diagnostic tasks. This substitution maintains high diagnostic capability by using intelligent computational methods while reducing system complexity from the user perspective, as the complex processing is encapsulated within the machine learning model rather than requiring complex user-side processing.
Data Source
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AI summary
At least some embodiments of the present disclosure is directed to a system for processing cardiac information. The system including a processing unit is configured to: receive a plurality of cardiac electrical signals collected from a plurality of electrodes disposed within a cardiac chamber, wherein the plurality of cardiac electrical signals are acquired over a cardiac beat having a cycle length; and calculate an importance metric for each of the plurality of the cardiac electrical signals, wherein the importance metric represents a contribution of a respective cardiac electrical signal to an overall duty cycle of the cardiac beat as a function of the cycle length.