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Related Concept Videos

Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

933
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow...
933

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Related Experiment Video

Updated: Jun 22, 2025

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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Artificial Intelligence-Based Atrial Fibrillation Recognition Method for Motion Artifact-Contaminated

Huanqian Zhang1, Hantao Zhao2, Zhang Guo2,3

  • 1Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.

Sensors (Basel, Switzerland)
|June 27, 2024
PubMed
Summary

Adaptive filtering significantly improves artificial intelligence detection of atrial fibrillation (AF) in electrocardiogram (ECG) signals corrupted by motion artifacts (MA). This method enhances AF recognition accuracy, crucial for wearable long-term ECG monitoring.

Keywords:
adaptive filteringartificial intelligenceatrial fibrillationelectrocardiogrammotion artifact

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Atrial fibrillation (AF) detection relies on long-term ECG monitoring.
  • Motion artifacts (MA) in ECG signals impede accurate AF diagnosis.
  • Existing AI algorithms effectively detect AF in clean ECG data.

Purpose of the Study:

  • To evaluate the impact of adaptive filtering (ADF) on AI-based AF recognition accuracy in the presence of MAs.
  • To determine if ADF can enhance AI performance for AF detection in noisy ECG signals.

Main Methods:

  • Artificially introduced 13 types of MA signals with varying signal-to-noise ratios into an AF ECG dataset.
  • Assessed AI AF recognition accuracy on ECG with MAs.
  • Applied ADF to remove MAs and re-evaluated AI AF recognition accuracy.

Main Results:

  • AI AF recognition accuracy was initially reduced by MA presence.
  • Post-ADF processing, AI AF recognition accuracy improved across all MA intensities.
  • Maximum accuracy improvement reached 60% after ADF application.

Conclusions:

  • Adaptive filtering is effective in mitigating motion artifact interference in ECG signals.
  • ADF preprocessing enhances the accuracy of AI algorithms for atrial fibrillation detection.
  • This approach holds promise for improving reliable, long-term wearable ECG monitoring for AF.