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Automated Detection of Normal, Atrial, and Ventricular Premature Beats from Single-Lead ECG Using Convolutional
1MedTec & Science GmbH, Maria-Merian-Straße 6, 85521 Ottobrunn, Germany.
Insights
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.
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.
Abstract:
Accurate detection of premature atrial contractions (PACs) and premature ventricular contractions (PVCs) in single-lead electrocardiograms (ECGs) is crucial for early identification of patients at risk for atrial fibrillation, cardiomyopathy, and other adverse outcomes. In this work, we present a fully convolutional one-dimensional U-Net that reframes beat classification as a segmentation task and directly detects normal beats, PACs, and PVCs from raw ECG signals. The architecture employs a ConvNeXt V2 encoder with simple decoder blocks and does not rely on explicit R-peak detection, handcrafted features, or fixed-length input windows. The model is trained on the Icentia11k database and an in-house single-lead ECG dataset that emphasizes challenging, noisy recordings, and is validated on the CPSC2020 database. Generalization is assessed across several benchmark and clinical datasets, including MIT-BIH Arrhythmia (ADB), MIT 11, AHA, NST, SVDB, CST STRIPS, and CPSC2020. The proposed method achieves near-perfect QRS detection (sensitivity and precision up to 0.999) and competitive PVC performance, with sensitivity ranging from 0.820 (AHA) to 0.986 (MIT 11) and precision up to 0.993 (MIT 11). PAC detection is more variable, with sensitivities between 0.539 and 0.797 and precisions between 0.751 and 0.910, yet the resulting F1-score of 0.72 on SVDB exceeds that of previously published approaches. Model interpretability is addressed using Layer-wise Gradient-weighted Class Activation Mapping (LayerGradCAM), which confirms physiologically plausible attention to QRS complexes for PVCs and to P-waves for PACs. Overall, the proposed framework provides a robust, interpretable, and hardware-efficient solution for joint PAC and PVC detection in noisy, single-lead ECG recordings, suitable for integration into Holter and wearable monitoring systems.
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