Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network

@article{Zahid2020RobustRD,
  title={Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network},
  author={Muhammad Uzair Zahid and Serkan Kiranyaz and Turker Ince and Ozer Can Devecioglu and Muhammad Enamul Hoque Chowdhury and Amith Khandakar and Anas M. Tahir and M. Gabbouj},
  journal={IEEE Transactions on Biomedical Engineering},
  year={2020},
  volume={69},
  pages={119-128}
}
Objective: Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signals. While many proposed algorithms have successfully addressed the problem of ECG R-peak detection, there is still a notable gap in the performance of these detectors on such low-quality ECG records. Methods: In this study, a novel implementation of… 

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