Cardiac Activation Waveform Analysis for Local Cycle Length Detection
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Solution Overview
Problem
Conventional cardiac mapping systems face challenges in accurately and efficiently interpreting large volumes of cardiac electrical signals due to the complexity of electrograms (EGMs) and the potential for electrical artifacts, leading to misleading maps and increased examination time and cost.
Innovation Solution
A method and system for processing cardiac information by determining local cycle length and duty cycle through iterative calculations of correlations and confidence values across varying window sizes, without the need for a fixed reference electrode, using a processing unit to analyze activation waveforms and select optimal window sizes based on correlations and confidence values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional mapping systems extract scalar values from each EGM to construct voltage, activation, or other map types, then the need to inspect captured EGMs is reduced, but the complex and useful information in the EGMs is condensed and may be misleading due to electrical artifacts or inappropriate selection of features
Solution Approach 1:
The patent segments the EGM signal into multiple time windows and analyzes each window separately to extract multiple features (amplitude, duration, morphology) rather than using a single scalar value. This segmentation allows preservation of detailed temporal information while enabling automated analysis, thus reducing examination time without losing diagnostic accuracy.
Solution Approach 2:
The patent transitions from scalar value extraction to multi-dimensional feature extraction by analyzing EGMs in multiple time windows and extracting multiple features per window (amplitude, duration, morphology). This dimensional expansion preserves comprehensive information while enabling automated pattern recognition, thereby maintaining diagnostic accuracy while reducing manual inspection time.
2Reliability
If approximately 6,000 to 20,000 intracardiac electrograms (EGMs) are captured during automatic electro-anatomical mapping, then comprehensive coverage is achieved, but manual inspection in full by a clinician becomes impractical
Solution Approach 1:
The patent creates compressed representations (copies) of the large volume of EGM data by extracting key features from multiple time windows and aggregating them into summary statistics and patterns. This copying approach preserves essential diagnostic information in a condensed format that can be efficiently analyzed automatically, making comprehensive coverage feasible without requiring manual inspection of all 6,000-20,000 EGMs.
Solution Approach 2:
The patent replaces manual visual inspection with automated computational analysis of multi-window EGM features. The system automatically extracts features from multiple time windows, compares patterns, and generates diagnostic conclusions, substituting the mechanical process of manual inspection with an automated information processing system that can handle 6,000-20,000 EGMs efficiently.
3Productivity
If maps are generated to reduce the need to inspect captured EGMs, then examination efficiency is improved, but the maps may be misleading due to electrical artifacts or inappropriate selection of features such as activation times
Solution Approach 1:
The patent employs dynamic feature selection by analyzing EGMs across multiple time windows and adaptively selecting relevant features based on local signal characteristics. Rather than using fixed activation time points, the system dynamically identifies feature locations and extracts multiple features per window, making the mapping process adaptive to local variations and less susceptible to artifacts from inappropriate fixed-point selection.
Solution Approach 2:
The patent changes the parameters used for map generation by extracting multiple features (amplitude, duration, morphology) from multiple time windows rather than relying on single scalar values like activation time. This parameter expansion provides a more robust representation that is less sensitive to electrical artifacts and inappropriate feature selection, thereby improving map accuracy while maintaining examination efficiency.
Data Source
AI summary
At least some embodiments of the present disclosure is directed to a method of processing cardiac information. The method includes the steps of: receiving an activation waveform comprising a set of activation waveform data; receiving a range of window size. For each of the plurality of window sizes, the method further includes the steps of: selecting a position of a central window; calculating a set of backward correlations; calculating a set of forward correlations; determining a backward confidence value based on the set of backward correlations; and determining a forward confidence value based on the set of forward correlations. Additionally, the method includes the steps of: comparing the set of confidence values to select a designated confidence value and a selected window size corresponding to the designated confidence value; and determining a local cycle length based on the selected window size.