Frequency Discrimination in Cardiac Activity Visualization
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Solution Overview
Problem
Current electro-anatomical mapping techniques lack effective visualization methods for identifying specific frequency bands of electro-cardiac signals, which are crucial for diagnosing heart pathologies like fibrillation and arrhythmias.
Innovation Solution
A system that measures and visualizes electro-cardiac signals by selecting a spectral slice using a user input device, calculating the levels of electrical activity within that slice, and displaying them on a heart map, with color-coding to represent different amplitudes, allowing for real-time identification of heart pathologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional electro-anatomical mapping techniques are used to measure electrical activity at multiple points on the heart surface, then spatial distribution of electrical signals can be obtained, but frequency discrimination and specific pathology identification are lacking
Solution Approach 1:
The frequency spectrum is segmented into multiple selectable bands, allowing the system to isolate and analyze specific frequency ranges associated with different cardiac pathologies. This segmentation enables precise frequency discrimination without requiring complex simultaneous multi-frequency analysis, thus improving measurement precision while managing system complexity through sequential spectral analysis.
Solution Approach 2:
The system applies partial action by focusing analysis on specific frequency bands of interest rather than processing the entire frequency spectrum uniformly. By selecting only the relevant spectral slices for display and analysis, the system achieves effective pathology identification without the computational burden of processing all frequency components with equal detail.
2Loss of information
If comprehensive electro-cardiac signals are measured across the heart surface, then complete electrical activity data is obtained, but direct visualization of frequency distribution is not achieved
Solution Approach 1:
The system extracts specific frequency band information from the comprehensive electro-cardiac signals by applying spectral analysis and isolating selected frequency slices. This extraction process separates the relevant frequency distribution information from the raw signal data, preventing information loss while enabling focused visualization of pathology-associated frequency patterns without displaying all raw signal details.
Solution Approach 2:
The system uses color-coded mapping to represent different frequency bands and amplitude levels on the electro-anatomical map. This visual encoding transforms abstract frequency distribution data into intuitive color patterns that are easily interpretable, significantly improving visualization ease while preserving complete frequency distribution information through the color spectrum.
3Reliability
If spectral analysis is performed on electro-cardiac signals to identify frequency bands, then pathology identification capability is improved, but real-time visualization is compromised
Solution Approach 1:
The system performs spectral analysis selectively on specific frequency bands of interest rather than conducting exhaustive analysis of the entire spectrum in real-time. This partial action approach maintains reliability for identifying pathology-associated frequency patterns while improving processing speed by focusing computational resources only on clinically relevant frequency ranges.
Solution Approach 2:
The system performs preliminary spectral analysis to identify dominant frequency characteristics before detailed pathology assessment. This preliminary action allows the system to quickly characterize the electrical activity and prepare frequency distribution data for visualization, enabling faster real-time or near-real-time presentation of pathology-relevant information without sacrificing identification accuracy.
Data Source
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Figure 3A~3B
AI summary
A method includes measuring electrical activity at multiple points on a surface of a heart of a patient. User input indicative of a spectral slice selected from a frequency band is received. Respective levels of the electrical activity within the selected spectral slice are calculated. The calculated levels are displayed on a map of the heart.