# Robustness of convolutional neural networks to physiological ECG noise

arXiv: https://arxiv.org/abs/2108.01995 · HTML: https://mlsanity.com/paper/2108.01995/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 5 of 5)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review](https://mlsanity.com/paper/2001.01550/) (2019)

## Built on by (most cited first, 7 of 7)

- [GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction](https://mlsanity.com/paper/2208.01220/) (2022)
- [A metrological framework for uncertainty evaluation in machine learning classification models](https://mlsanity.com/paper/2504.03359/) (2025)
- [Analytical results for uncertainty propagation through trained machine learning regression models](https://mlsanity.com/paper/2404.11224/) (2024)
- [Robustness Enhancement in Neural Networks with Alpha-Stable Training Noise](https://mlsanity.com/paper/2311.10803/) (2023)
- [Benchmarking the Impact of Noise on Deep Learning-based Classification of Atrial Fibrillation in 12-Lead ECG](https://mlsanity.com/paper/2303.13915/) (2023)
- [Continuous Wavelet Transformation and VGG16 Deep Neural Network for Stress Classification in PPG Signals](https://mlsanity.com/paper/2410.14747/) (2024)
- [Multimodal signal fusion for stress detection using deep neural networks: a novel approach for converting 1D signals to unified 2D images](https://mlsanity.com/paper/2509.13636/) (2025)

## Abstract (arXiv)

The electrocardiogram (ECG) is one of the most widespread diagnostic tools in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. However, there are open questions around the robustness of these methods to various factors, including physiological ECG noise. In this study we generate clean and noisy versions of an ECG dataset before applying Symmetric Projection Attractor Reconstruction (SPAR) and scalogram image transformations. A pretrained convolutional neural network is trained using transfer learning to classify these image transforms. For the clean ECG dataset, F1 scores for SPAR attractor and scalogram transforms were 0.70 and 0.79, respectively, and the scores decreased by less than 0.05 for the noisy ECG datasets. Notably, when the network trained on clean data was used to classify the noisy datasets, performance decreases of up to 0.18 in F1 scores were seen. However, when the network trained on the noisy data was used to classify the clean dataset, the performance decrease was less than 0.05. We conclude that physiological ECG noise impacts classification using deep learning methods and careful consideration should be given to the inclusion of noisy ECG signals in the training data when developing supervised networks for ECG classification.