Heart Rhythm Driver Identification Using Geodesic Electrogram Analysis
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Solution Overview
Problem
Current methods for identifying areas responsible for abnormal heart rhythms in atrial fibrillation are hindered by the chaotic and irregular nature of activation wavefronts, leading to inaccurate ablation sites and increased procedural risks due to discontinuous conduction and aliasing effects in 3D heart anatomy.
Innovation Solution
A computer-implemented method using electrogram activation data from multiple electrodes, which sets a geodesic distance to pair sensing locations, determines relative timing, and assigns lead signal scores based on the proportion of time each activation signal leads within these pairings, providing a statistical measure of likelihood for each location to be a driver area of abnormal heart rhythms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classification based on neighbouring activations to sensors is used, then focal activation areas can be highlighted in 2D models, but aliasing effects occur in 3D heart anatomy leading to incorrect ablation sites
Solution Approach 1:
The patent transitions from 2D representations to 3D geodesic distance calculations on the actual heart surface geometry. By using geodesic distances that account for the curved 3D anatomy of the heart, the system avoids aliasing effects that occur when assuming flat 2D spatial relationships, thereby improving the accuracy of driver area identification in complex 3D cardiac structures.
Solution Approach 2:
The patent introduces an intermediary computational framework that processes electrogram data through multiple analysis time periods and overlapping geodesic areas. This intermediary processing layer combines lead signal scores from multiple perspectives and time points, filtering out false positives from aliasing effects before presenting the final driver area identification to the clinician.
2Ease of manufacture
If simple neighbouring sensor comparison is used, then the method is easy to implement, but it fails to account for discontinuous and circuitous conduction in 3D atrial wall
Solution Approach 1:
The patent implements dynamic analysis by dividing the recording period into multiple overlapping analysis time periods and recalculating lead signal scores for each period. This dynamic approach allows the system to adapt to changing conduction patterns in atrial fibrillation, capturing circuitous and discontinuous conduction pathways that static single-period analysis would miss.
Solution Approach 2:
The patent performs preliminary calculations of geodesic distances and conduction velocities before analyzing activation sequences. By pre-computing the actual 3D distances along heart surface and establishing plausible conduction velocity ranges, the system creates a framework that automatically accounts for complex conduction pathways without requiring complex real-time calculations during activation analysis.
3Productivity
If single time period analysis is used, then processing is fast, but overlapping activations across time periods are not captured
Solution Approach 1:
The patent applies periodic action by analyzing multiple discrete, overlapping time periods sequentially. Each time period is analyzed independently to calculate lead signal scores, then the results are combined through statistical aggregation. This periodic approach balances computational efficiency with comprehensive data coverage, capturing transient driver activities that might be missed in continuous analysis.
Solution Approach 2:
The patent merges results from multiple overlapping analysis time periods by combining lead signal scores through statistical measures. By aggregating data across overlapping time windows, the system increases the statistical reliability of driver area identification while maintaining the computational efficiency of discrete period analysis, as overlapping regions provide redundant confirmation of persistent drivers.
Data Source
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AI summary
A computer implemented method and system for identifying one or more areas of the heart muscle responsible for supporting or initiating abnormal heart rhythms using electrogram data recorded from a plurality of electrodes; the method including the steps of: dividing the recording time period into several analysis time periods, and pairing each sensing location with a plurality of other sensing locations from within a defined distance; for each of the analysis time periods, defining the relative timing of each activation signal for each location within each pairing, defining the leading signal of the pair for each electrogram activation within the respective analysis time period; and assigning a series of lead signal scores to each electrogram pairing; repeating the analysis at the same location at least once whilst varying the analysis time period; combining each analysis time period for each signal location to provide a statistical measure of the proportion that each signal location tends to lead relative to other locations within the defined area; and relating lead signal scores from overlapping areas to provide relative combined lead signal scores; to provide an indication of the relative likelihood that each sensing location is generally preceding other areas.