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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: Jan 29, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Automated Detection of Normal, Atrial, and Ventricular Premature Beats from Single-Lead ECG Using Convolutional

Dimitri Kraft1, Peter Rumm2

  • 1MedTec & Science GmbH, Maria-Merian-Straße 6, 85521 Ottobrunn, Germany.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

A novel U-Net model accurately detects premature atrial and ventricular contractions from ECGs without R-peak detection. This advancement aids in early risk identification for serious heart conditions.

Keywords:
1D U-Net neural networkHolter monitoringPremature Atrial Contraction (PAC) detectionPremature Ventricular Contraction (PVC) detection

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Accurate detection of premature atrial contractions (PACs) and premature ventricular contractions (PVCs) from single-lead electrocardiograms (ECGs) is vital for identifying patients at risk of atrial fibrillation and cardiomyopathy.
  • Current methods may rely on R-peak detection or handcrafted features, limiting their applicability in noisy or complex ECG signals.

Purpose of the Study:

  • To present a fully convolutional one-dimensional U-Net model for direct detection of normal beats, PACs, and PVCs from raw single-lead ECG signals.
  • To evaluate the model's performance on diverse datasets, including challenging noisy recordings, and assess its generalization capabilities.

Main Methods:

  • A U-Net architecture with a ConvNeXt V2 encoder and simple decoder blocks was employed, reframing beat classification as a segmentation task.
  • The model was trained on the Icentia11k and an in-house ECG dataset, and validated on CPSC2020, with generalization tested on multiple benchmark datasets.
  • No explicit R-peak detection, handcrafted features, or fixed-length input windows were utilized.

Main Results:

  • The model achieved near-perfect QRS detection (sensitivity and precision up to 0.999).
  • Competitive PVC detection performance was observed, with sensitivities up to 0.986 and precision up to 0.993 across datasets.
  • PAC detection showed variability but achieved an F1-score of 0.72 on the SVDB dataset, surpassing previous methods. LayerGradCAM confirmed physiologically plausible attention mechanisms.

Conclusions:

  • The proposed U-Net framework offers a robust, interpretable, and hardware-efficient solution for joint PAC and PVC detection in noisy single-lead ECGs.
  • The method is suitable for integration into continuous monitoring systems like Holter monitors and wearables.
  • This approach advances automated cardiac arrhythmia detection, potentially improving early diagnosis and patient outcomes.