# RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG

arXiv: https://arxiv.org/abs/2004.08103 · HTML: https://mlsanity.com/paper/2004.08103/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Joint optimization of a β-VAE for ECG task-specific feature extraction](https://mlsanity.com/paper/2304.06476/) (2023)
- [Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error](https://mlsanity.com/paper/2107.00693/) (2021)
- [Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Sign](https://mlsanity.com/paper/2605.16452/) (2026)
- [Dynamic-Deep: Tune ECG Task Performance and Optimize Compression in IoT Architectures](https://mlsanity.com/paper/2106.00606/) (2021)
- [A Complex UNet Approach for Non-Invasive Fetal ECG Extraction Using Single-Channel Dry Textile Electrodes](https://mlsanity.com/paper/2506.22457/) (2025)

## Abstract (arXiv)

Automatic detection of R-peaks in an Electrocardiogram signal is crucial in a multitude of applications including Heart Rate Variability (HRV) analysis and Cardio Vascular Disease(CVD) diagnosis. Although there have been numerous approaches that have successfully addressed the problem, there has been a notable dip in the performance of these existing detectors on ECG episodes that contain noise and HRV Irregulates. On the other hand, Deep Learning(DL) based methods have shown to be adept at modelling data that contain noise. In image to image translation, Unet is the fundamental block in many of the networks. In this work, a novel application of the Unet combined with Inception and Residual blocks is proposed to perform the extraction of R-peaks from an ECG. Furthermore, the problem formulation also robustly deals with issues of variability and sparsity of ECG R-peaks. The proposed network was trained on a database containing ECG episodes that have CVD and was tested against three traditional ECG detectors on a validation set. The model achieved an F1 score of 0.9837, which is a substantial improvement over the other beat detectors. Furthermore, the model was also evaluated on three other databases. The proposed network achieved high F1 scores across all datasets which established its generalizing capacity. Additionally, a thorough analysis of the model's performance in the presence of different levels of noise was carried out.