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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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[Approximate entropy of electrocardiogram signals in atrial fibrillation]

Takuya Horie1

  • 1Department of Pathobiological Science and Technology, School of Health Science, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan. aoitoriwosagasite77@yahoo.co.jp

Rinsho Byori. the Japanese Journal of Clinical Pathology
|December 31, 2013
PubMed

Insights

Approximate entropy (ApEn) analysis of electrocardiogram (ECG) signals reveals higher randomness in patients with atrial fibrillation (Af). This method effectively assesses the nonlinear properties of ECG, aiding in Af evaluation.

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Nonlinear Dynamics

Context:

  • Atrial fibrillation (Af) is a prevalent cardiac arrhythmia associated with a significant risk of thromboembolism, necessitating anticoagulant therapy.
  • Electrocardiogram (ECG) signals reflect the heart's electrical activity and can be analyzed for complex patterns.
  • Understanding the nonlinear properties of cardiac signals may offer new diagnostic insights.

Purpose:

  • To investigate the utility of Approximate Entropy (ApEn) in quantifying the complexity and randomness of cardiac electrical activity in patients with chronic atrial fibrillation (Af).
  • To compare ApEn values derived from ECG signals between individuals with and without Af.
  • To assess the influence of anti-arrhythmic drug administration on ECG signal complexity as measured by ApEn.

Summary:

  • The study calculated Approximate Entropy (ApEn) for ECG signals from 29 subjects without arrhythmia and 12 patients with chronic Af.
  • Mean ApEn was significantly higher in the chronic Af group (0.556 +/- 0.169) compared to the control group (0.347 +/- 0.119) (p < 0.002).
  • ApEn was also significantly higher in patients not on anti-arrhythmic drugs (0.687 +/- 0.033) versus those on medication (0.516 +/- 0.209) (p < 0.05), indicating ApEn's effectiveness in evaluating ECG nonlinear properties and assessing Af randomness.

Impact:

  • Approximate Entropy (ApEn) serves as a valuable tool for evaluating the nonlinear characteristics of ECG signals.
  • This analysis provides a quantitative measure to assess the randomness associated with chronic atrial fibrillation.
  • Findings suggest ApEn could enhance the diagnostic assessment of Af and potentially guide treatment strategies.

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