Spatiotemporal Electro-Anatomical Map for Arrhythmia Classification
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Solution Overview
Problem
Current electro-anatomical mapping techniques for cardiac chambers are labor-intensive and time-consuming, requiring manual examination to identify arrhythmia origins and paths, which hampers efficient diagnosis and treatment.
Innovation Solution
A method and system for generating a spatiotemporal electro-anatomical map that classifies tissue locations based on rhythmic patterns by comparing cardiac cycle lengths over time, graphically encoding normal and aberrant regions to assist in identifying arrhythmia origins and types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual examination methods are used to identify arrhythmia origins and paths, then diagnostic accuracy can be achieved, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system performs automatic classification of tissue locations based on rhythmic patterns, enabling the mapping system to diagnose itself without requiring manual examination by physicians. The processor automatically compares cardiac cycle lengths, identifies timing patterns, and classifies locations as normal or aberrant, making the system self-diagnostic while maintaining accuracy
Solution Approach 2:
The system pre-processes and classifies timing patterns from electrogram signals before clinical interpretation is needed. By automatically generating the spatiotemporal map and identifying arrhythmia origins in advance, the system prepares diagnostic information ready for rapid clinical decision-making, reducing the time physicians need to spend on manual analysis
2Productivity
If automatic classification methods are implemented, then diagnostic efficiency is improved, but system complexity increases
Solution Approach 1:
The diagnostic process is segmented into distinct automated stages: signal acquisition from electrogram channels, cardiac cycle length calculation, timing pattern comparison, and classification decision-making. Each stage processes specific data independently, allowing the complex diagnostic task to be broken down into manageable computational steps that can be executed automatically without overwhelming system complexity
Solution Approach 2:
The system transforms raw electrogram signals into derived parameters (cardiac cycle lengths), then into timing patterns, and finally into classification categories (normal/aberrant). This parameter transformation chain converts complex physiological data into simplified diagnostic categories through systematic parameter changes, enabling automatic classification while managing computational complexity
Data Source
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AI summary
A method includes receiving an electrocardiogram (ECG) measured at a given location over a portion of a heart. Based on the measured ECG, a rhythmic pattern is identified over a given time-interval. The rhythmic pattern corresponds to a relation between a present cardiac cycle length and a preceding cardiac cycle length. Based on the identified rhythmic pattern, a classification of the location as either showing regular pattern or showing arrhythmia is determined. The location is graphically encoded according to the classification. The graphically encoded location is overlaid on an anatomical map of the portion of a heart.