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Related Concept Videos

Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
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Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
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Related Experiment Video

Updated: May 25, 2026

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
09:17

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

Published on: July 29, 2011

Propagation pattern analysis during atrial fibrillation based on sparse modeling.

Ulrike Richter1, Luca Faes, Flavia Ravelli

  • 1Department of Electrical and Information Technology, Lund University, 22100 Lund, Sweden. ulrike.richter@med.lu.se

IEEE Transactions on Bio-Medical Engineering
|February 14, 2012
PubMed
Summary
This summary is machine-generated.

Sparse modeling improves atrial fibrillation (AF) signal analysis by accurately estimating propagation patterns. New methods, aLASSO and dLASSO, significantly reduce estimation errors compared to traditional least-squares methods.

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Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Computational Cardiology

Background:

  • Atrial fibrillation (AF) propagation patterns are complex and challenging to estimate.
  • Traditional methods like least-squares (LS) estimation have limitations in accuracy and performance.
  • Sparse modeling offers a promising approach to improve signal analysis.

Purpose of the Study:

  • To introduce sparse modeling for estimating intracardiac atrial fibrillation (AF) propagation patterns.
  • To develop and evaluate novel optimization methods, adaptive group LASSO (aLASSO) and distance-adaptive group LASSO (dLASSO).
  • To compare the performance of these new methods against traditional LS estimation.

Main Methods:

  • Utilizing partial directed coherence derived from multivariate autoregressive models.
  • Incorporating prior information on sparse coupling and recording site distances.
  • Employing adaptive group LASSO (aLASSO) and distance-adaptive group LASSO (dLASSO) for parameter estimation.

Main Results:

  • Both aLASSO and dLASSO significantly outperformed LS estimation in detection and estimation accuracy.
  • Normalized error decreased from 0.20 ± 0.04 (LS) to 0.03 ± 0.01 (aLASSO, dLASSO) with sufficient data.
  • Error reduction was more pronounced with shorter data segments, highlighting the importance of distance information.

Conclusions:

  • Sparse modeling substantially simplifies the identification of AF propagation patterns.
  • aLASSO and dLASSO provide a more accurate and robust estimation of signal propagation.
  • These advanced methods hold potential for improved diagnosis and understanding of atrial fibrillation.