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Updated: Oct 22, 2025

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

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Blind Source Separation in Persistent Atrial Fibrillation Electrocardiograms Using Block-Term Tensor Decomposition

P M R de Oliveira, J H de M Goulart, C A R Fernandes

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|August 30, 2021

Summary

This summary is machine-generated.

This study introduces a new tensor method to accurately estimate atrial activity (AA) from electrocardiogram (ECG) signals, improving atrial fibrillation (AF) analysis. The novel approach effectively separates and removes QRS complexes for clearer AA signal extraction.

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Area of Science:

Biomedical Engineering
Signal Processing
Computational Cardiology

Background:

Atrial fibrillation (AF) analysis relies on estimating atrial activity (AA) from ECGs.
Existing tensor factorization methods like Hankel-based block term decomposition (BTD) struggle with weak AA in persistent AF.
Persistent AF presents challenges due to short R-R intervals and disorganized AA signals.

Purpose of the Study:

To develop a novel tensor-based approach for improved atrial activity (AA) signal estimation in electrocardiogram (ECG) recordings.
To address the limitations of existing methods in analyzing persistent atrial fibrillation (AF) with weak AA signals.
To enhance the noninvasive analysis of AF by accurately separating AA from ECG data.

Main Methods:

Proposed a tensor approach to estimate and subtract QRS complexes from ECG signals, isolating the AA component.
Formulated the problem as blind separation of rational functions, modeling QRS complexes explicitly.
Developed a variant of the constrained alternating group lasso (CAGL) algorithm, incorporating Löwner structure via orthogonal projection (LCAGL).

Main Results:

The proposed Löwner-constrained AGL (LCAGL) algorithm demonstrated consistency in extracting desired sources from synthetic data.
Experiments on 20 persistent AF patients showed LCAGL outperforms other tensor-based methods.
The method achieved superior atrial signal estimation quality, even from single-heartbeat ECG records.

Conclusions:

The novel tensor approach effectively estimates atrial activity (AA) by first separating QRS complexes.
This method overcomes limitations of prior techniques for analyzing persistent atrial fibrillation (AF).
The proposed LCAGL algorithm offers a significant advancement in ECG-based AF analysis.