Cardiac Mapping Catheter Dominant Frequency Identification
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Solution Overview
Problem
Current medical devices for mapping and ablating cardiac tissue lack effective methods for accurately identifying and visualizing dominant frequency patterns, which are crucial for diagnosing and treating heart rhythm disorders like atrial fibrillation.
Innovation Solution
A system comprising a catheter with multiple electrodes and a processor that generates time-frequency distributions and identifies dominant frequency values, using transforms like Fourier or Wavelet, to determine attraction points and create visual displays for guiding ablation therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional electrogram analysis methods are used, then the system is simple to operate, but the measurement precision of dominant frequency patterns is insufficient
Solution Approach 1:
The patent replaces traditional time-domain signal analysis with frequency-domain analysis using Fourier transforms and wavelet transforms. This substitution enables precise identification of dominant frequency patterns by converting temporal electrogram signals into frequency spectra, where dominant frequencies can be objectively identified through spectral peaks rather than subjective visual inspection of waveforms.
Solution Approach 2:
The patent introduces time-frequency distributions that add a frequency dimension to the traditional time-domain analysis. By generating spectrograms and other time-frequency representations, the system transforms one-dimensional temporal signals into two-dimensional time-frequency maps, enabling simultaneous observation of frequency content evolution over time and precise identification of dominant frequency patterns across multiple dimensions.
2Measurement precision
If multiple electrodes are used to collect signals, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent combines signals from multiple electrodes by generating time-frequency distributions for each electrode and identifying attraction points where dominant frequency patterns converge. This merging approach integrates spatial information from multiple sensing locations while using frequency-domain analysis to synthesize the data, reducing the complexity of processing multiple electrode signals compared to traditional point-by-point analysis.
Solution Approach 2:
The patent creates visual representations and time-frequency distribution copies of electrical activity from multiple electrodes, allowing simultaneous comparison of frequency patterns across different spatial locations. These graphical copies enable clinicians to identify spatial relationships and propagation patterns without directly analyzing raw multi-electrode data, simplifying the interpretation process while maintaining high spatial resolution.
3Measurement precision
If Fourier transform or Wavelet transform is applied, then the measurement precision of frequency identification is improved, but the use of energy by the processor increases
Solution Approach 1:
The patent applies partial Fourier or Wavelet transforms to identify only the dominant frequency components rather than performing complete spectral analysis across all frequencies. By focusing computational resources on identifying peak frequency values and their temporal evolution rather than analyzing the entire frequency spectrum in detail, the system achieves sufficient measurement precision for clinical decision-making while reducing processor energy consumption.
Data Source
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AI summary
Medical devices and methods for making and using medical devices are disclosed. An example system for mapping the electrical activity of the heart includes a catheter shaft. The catheter shaft includes a plurality of electrodes including a first electrode and a second electrode. The system also includes a processor. The processor is capable of collecting a first signal corresponding to the first electrode and a second signal corresponding to the second electrode. Collecting the first and second signals occurs over a time period. The processor is also capable of generating a first time-frequency distribution corresponding to the first signal, identifying a first dominant frequency value occurring at a first dominant frequency and a first time point, generating a second time-frequency distribution corresponding to the second signal, identifying a second dominant frequency value occurring at a second dominant frequency and a second time point and determining an attraction point.