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. Therefore, in this study, a novel implementation of the 1D Convolutional Neural Network (CNN) is used integrated with a verification model to reduce the number of false alarms. This CNN architecture consists of an encoder block and a corresponding decoder block followed by a sample-wise classification layer to construct the 1D segmentation map of R- peaks from the input ECG signal. Once the proposed model has been trained, it can solely be used to detect R-peaks possibly in a single channel ECG data stream quickly and accurately, or alternatively, such a solution can be conveniently employed for real-time monitoring on a lightweight portable device. The model is tested on two open-access ECG databases: The China Physiological Signal Challenge (2020) database (CPSC-DB) with more than one million beats, and the commonly used MIT-BIH Arrhythmia Database (MIT-DB). Experimental results demonstrate that the proposed systematic approach achieves 99.30% F1-score, 99.69% recall, and 98.91% precision in CPSC-DB, which is the best R-peak detection performance ever achieved. Compared to all competing methods, the proposed approach can reduce the false-positives and false-negatives in Holter ECG signals by more than 54% and 82%, respectively. Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99.83% F1-score, 99.85% recall, and 99.82% precision.

翻译：从霍尔特或可磨损装置获得的ECG信号的声响和低质量使R峰级检测算法的准确性和稳健性下降。本文展示了Holter ECG信号中R峰级检测的通用和稳健系统。虽然许多拟议的算法成功地解决了ECG R峰级检测问题,但在这类低质量ECG记录上,这些探测器的性能仍然存在显著差距。因此,在这项研究中,1D 革命神经网络(CNN)的新实施与核查模型相结合,以减少R峰级准确度检测算法的准确性和稳健性。这个CNN结构包括一个编码器块和相应的解码块,随后又有一个样本化分类层,以构建输入ECG信号的R峰值的1D分区图。一旦对拟议的模型进行了培训,它只能用来在单一的ECG数据流中快速准确地检测R峰值。 或者说,这种解决方案可以方便地用来实时监测轻度的 RCR-CP 30 移动式设备的数量。在两个开放的 ECG数据库中测试了(20 % ) 和普通的OIDB数据库中, 展示了最常用的运行ADBDBDBM 。