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Spiral waves characterization: Implications for an automated cardiodynamic tissue characterization.

Celal Alagoz1, Andrew R Cohen1, Daniel R Frisch2

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|June 2, 2018
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Summary

This study introduces a new method to analyze cardiac spiral wave behaviors using electrogram (EGM) readings from standard catheters. The approach successfully distinguishes different rotor types, offering a potential new framework for cardiac analysis.

Keywords:
Cardiac electro-physiology simulationCardiac fibrillationClassificationClusteringIntracardiac electrogramsNormalized compression distanceSpiral waves

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Area of Science:

  • Cardiovascular Physiology
  • Computational Biology
  • Biomedical Engineering

Background:

  • Spiral waves are critical phenomena in cardiac tissue, particularly during fibrillation.
  • Current methods for spiral wave detection, such as high-density mapping, require specialized equipment.
  • In-silico analysis often relies on comprehensive membrane potential data from entire tissues.

Purpose of the Study:

  • To develop and validate a novel characterization approach for identifying spiral wave behaviors.
  • To utilize intracardiac electrogram (EGM) readings from common diagnostic catheters for localized, high-resolution analysis.
  • To distinguish between stationary, meandering, and break-up rotor types.

Main Methods:

  • Clustering and classification algorithms applied to simulated cardiac propagation data.
  • Modeling of unipolar-bipolar EGM readings using two catheter types.
  • Assessment of spiral wave behavior distances using normalized compression distance (NCD) and normalized FFT distance (NFFTD).

Main Results:

  • High clustering performance achieved across various EGM reading configurations.
  • NCD demonstrated superior effectiveness in distinguishing spiral wave behaviors compared to NFFTD.
  • Successful identification of distinct spiral activities in behaviorally heterogeneous cardiac tissue.

Conclusions:

  • The study theoretically validates clustering and classification approaches for automated EGM signal analysis.
  • This method provides a potential framework for mapping EGM signals to spiral wave behaviors.
  • Offers a new analysis tool for understanding cardiac tissue wavefront propagation patterns.