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Related Experiment Video

Updated: Jun 26, 2026

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

Published on: July 29, 2011

Feature extraction of the atrial fibrillation signal using the continuous wavelet transform.

Ken W Lee¹, Thomas H Everett, H Tolga Ilhan

¹Div. of Cardiology, California Univ., San Francisco, CA, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 3, 2007

Summary

Atrial fibrillation, a common heart rhythm disorder, presents challenges in treatment. New analysis methods reveal organization within this arrhythmia, potentially leading to better therapies for cardiovascular health.

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Last Updated: Jun 26, 2026

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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

Published on: July 20, 2022

Area of Science:

Cardiology
Biomedical Engineering
Signal Processing

Background:

Atrial fibrillation is a primary cause of cardiovascular morbidity and mortality.
Despite advances, effective management of atrial fibrillation remains a significant clinical challenge.
Periods of organization exist within the chaotic atrial fibrillation signal, but analysis is limited.

Purpose of the Study:

To address the limitations in analyzing atrial fibrillation signals due to lack of time-frequency resolution.
To explore the utility of the continuous wavelet transform for analyzing atrial fibrillation.
To investigate temporal and spatial information regarding arrhythmia organization.

Main Methods:

Utilizing the continuous wavelet transform (CWT) for signal analysis.
Applying CWT to high-density atrial mappings of atrial fibrillation.
Leveraging CWT's time-frequency multi-resolution capabilities.

Main Results:

The continuous wavelet transform provides enhanced time-frequency resolution for atrial fibrillation signals.
CWT analysis reveals previously uncharacterized temporal and spatial patterns of organization.
This method offers a novel approach to understanding arrhythmia dynamics.

Conclusions:

The continuous wavelet transform is a valuable tool for analyzing complex cardiac arrhythmias like atrial fibrillation.
Understanding organization within atrial fibrillation may pave the way for novel therapeutic strategies.
Further research using CWT could improve patient outcomes in cardiovascular care.