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<p>Premature ventricular contraction (PVC) is a common cardiac arrhythmia, and its timely and automated detection is crucial for preventing life-threatening cardiovascular events and reducing clinical workload in long-term monitoring. This paper reviews state-of-the-art PVC detection methods based on electrocardiogram (ECG) and photoplethysmogram (PPG) signals, employing signal processing techniques, traditional machine learning, and deep learning approaches. The existing methods are categorized into three groups: threshold-based or heuristic-based techniques, conventional machine learning models, and deep learning frameworks. Additionally, the paper provides an overview of ECG and PPG signal databases and the benchmark metrics used for performance evaluation. Given that most methods utilize R-peak and systolic peak detection during preprocessing, we also review various preprocessing techniques for detecting R-peaks in ECG signals and systolic peaks in PPG signals. Based on the performance and key contributions of existing PVC detection methods, we highlight major challenges and future directions, considering the presence of various noise sources in ECG and PPG signals—particularly under ambulatory and exercise conditions—and the resource constraints of wearable or portable long-term ECG and/or PPG monitoring devices.</p>

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ECG and PPG Signals-Based Premature Ventricular Contraction Detection Methods: A Review, Key Challenges, and Future Directions

  • 期刊论文

  • 发表日期：

    2026年2月3日

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  • Shailesh Mohine,
  • Nabasmita Phukan,
  • M. Sabarimalai Manikandan,
  • Ram Bilas Pachori

摘要

Premature ventricular contraction (PVC) is a common cardiac arrhythmia, and its timely and automated detection is crucial for preventing life-threatening cardiovascular events and reducing clinical workload in long-term monitoring. This paper reviews state-of-the-art PVC detection methods based on electrocardiogram (ECG) and photoplethysmogram (PPG) signals, employing signal processing techniques, traditional machine learning, and deep learning approaches. The existing methods are categorized into three groups: threshold-based or heuristic-based techniques, conventional machine learning models, and deep learning frameworks. Additionally, the paper provides an overview of ECG and PPG signal databases and the benchmark metrics used for performance evaluation. Given that most methods utilize R-peak and systolic peak detection during preprocessing, we also review various preprocessing techniques for detecting R-peaks in ECG signals and systolic peaks in PPG signals. Based on the performance and key contributions of existing PVC detection methods, we highlight major challenges and future directions, considering the presence of various noise sources in ECG and PPG signals—particularly under ambulatory and exercise conditions—and the resource constraints of wearable or portable long-term ECG and/or PPG monitoring devices.