  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.
