# Robust Peak Detection for Holter ECGs by Self-Organized Operational Neural Networks

arXiv: https://arxiv.org/abs/2110.02381 · HTML: https://mlsanity.com/paper/2110.02381/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 72 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Self-Organized Operational Neural Networks with Generative Neurons](https://mlsanity.com/paper/2004.11778/) (2020)
- [Operational Neural Networks](https://mlsanity.com/paper/1902.11106/) (2019)
- [Self-Organized Operational Neural Networks for Severe Image Restoration Problems](https://mlsanity.com/paper/2008.12894/) (2020)
- [FastONN -- Python based open-source GPU implementation for Operational Neural Networks](https://mlsanity.com/paper/2006.02267/) (2020)
- [Heterogeneous Multilayer Generalized Operational Perceptron](https://mlsanity.com/paper/1804.05093/) (2018)
- [Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs](https://mlsanity.com/paper/2109.13604/) (2021)
- [Progressive Operational Perceptron with Memory](https://mlsanity.com/paper/1808.06377/) (2018)
- [Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network](https://mlsanity.com/paper/2101.01666/) (2020)

## Built on by (most cited first, 11 of 11)

- [Global ECG Classification by Self-Operational Neural Networks with Feature Injection](https://mlsanity.com/paper/2204.03768/) (2022)
- [A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance](https://mlsanity.com/paper/2207.07089/) (2022)
- [Zero-Shot Motor Health Monitoring by Blind Domain Transition](https://mlsanity.com/paper/2212.06154/) (2022)
- [Blind Restoration of Real-World Audio by 1D Operational GANs](https://mlsanity.com/paper/2212.14618/) (2022)
- [ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning](https://mlsanity.com/paper/2304.04646/) (2023)
- [2D Self-Organized ONN Model For Handwritten Text Recognition](https://mlsanity.com/paper/2207.08139/) (2022)
- [Axiomatizing Neural Networks via Pursuit of Subspaces](https://mlsanity.com/paper/2605.20534/) (2026)
- [Exploring Sound vs Vibration for Robust Fault Detection on Rotating Machinery](https://mlsanity.com/paper/2312.10742/) (2023)
- [High-Quality and Full Bandwidth Seismic Signal Synthesis using Operational GANs](https://mlsanity.com/paper/2407.11040/) (2024)
- [BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial Network](https://mlsanity.com/paper/2407.13949/) (2024)
- [Multi-Pass, Multi-View Blended Learning for High-Fidelity Volumetric CT Synthesis from Chest X-Rays](https://mlsanity.com/paper/2609.09920/) (2026)

## Abstract (arXiv)

Although numerous R-peak detectors have been proposed in the literature, their robustness and performance levels may significantly deteriorate in low-quality and noisy signals acquired from mobile electrocardiogram (ECG) sensors, such as Holter monitors. Recently, this issue has been addressed by deep 1-D convolutional neural networks (CNNs) that have achieved state-of-the-art performance levels in Holter monitors; however, they pose a high complexity level that requires special parallelized hardware setup for real-time processing. On the other hand, their performance deteriorates when a compact network configuration is used instead. This is an expected outcome as recent studies have demonstrated that the learning performance of CNNs is limited due to their strictly homogenous configuration with the sole linear neuron model. In this study, to further boost the peak detection performance along with an elegant computational efficiency, we propose 1-D Self-Organized ONNs (Self-ONNs) with generative neurons. The most crucial advantage of 1-D Self-ONNs over the ONNs is their self-organization capability that voids the need to search for the best operator set per neuron since each generative neuron has the ability to create the optimal operator during training. The experimental results over the China Physiological Signal Challenge-2020 (CPSC) dataset with more than one million ECG beats show that the proposed 1-D Self-ONNs can significantly surpass the state-of-the-art deep CNN with less computational complexity. Results demonstrate that the proposed solution achieves a 99.10% F1-score, 99.79% sensitivity, and 98.42% positive predictivity in the CPSC dataset, which is the best R-peak detection performance ever achieved.