# Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks

arXiv: https://arxiv.org/abs/2303.11338 · HTML: https://mlsanity.com/paper/2303.11338/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 71 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Domain Generalization by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1903.06864/) (2019)
- [Self-Challenging Improves Cross-Domain Generalization](https://mlsanity.com/paper/2007.02454/) (2020)
- [Return of Frustratingly Easy Domain Adaptation](https://mlsanity.com/paper/1511.05547/) (2015)
- [Learning to Optimize Domain Specific Normalization for Domain Generalization](https://mlsanity.com/paper/1907.04275/) (2019)
- [Deep Stable Learning for Out-Of-Distribution Generalization](https://mlsanity.com/paper/2104.07876/) (2021)
- [Hypercolumns for Object Segmentation and Fine-grained Localization](https://mlsanity.com/paper/1411.5752/) (2014)
- [Uncovering the structure of clinical EEG signals with self-supervised learning](https://mlsanity.com/paper/2007.16104/) (2020)
- [Learning to Learn with Variational Information Bottleneck for Domain Generalization](https://mlsanity.com/paper/2007.07645/) (2020)
- [Self-supervised ECG Representation Learning for Emotion Recognition](https://mlsanity.com/paper/2002.03898/) (2020)
- [Self-supervised representation learning from 12-lead ECG data](https://mlsanity.com/paper/2103.12676/) (2021)
- [Self-supervised representation learning from electroencephalography signals](https://mlsanity.com/paper/1911.05419/) (2019)
- [Learning Domain-Invariant Subspace using Domain Features and Independence Maximization](https://mlsanity.com/paper/1603.04535/) (2016)

## Built on by (most cited first, 14 of 14)

- [Gradient-Guided Annealing for Domain Generalization](https://mlsanity.com/paper/2502.20162/) (2025)
- [Generalization in medical AI: a perspective on developing scalable models](https://mlsanity.com/paper/2311.05418/) (2023)
- [RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG](https://mlsanity.com/paper/2401.05411/) (2023)
- [CNN Feature Map Augmentation for Single-Source Domain Generalization](https://mlsanity.com/paper/2305.16746/) (2023)
- [Multi-Scale and Multi-Layer Contrastive Learning for Domain Generalization](https://mlsanity.com/paper/2308.14418/) (2023)
- [Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning](https://mlsanity.com/paper/2312.00502/) (2023)
- [Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging](https://mlsanity.com/paper/2312.03196/) (2023)
- [SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography](https://mlsanity.com/paper/2404.06869/) (2024)
- [CycleMix: Mixing Source Domains for Domain Generalization in Style-Dependent Data](https://mlsanity.com/paper/2407.13421/) (2024)
- [uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm](https://mlsanity.com/paper/2506.11238/) (2025)
- [Towards Generalizable Learning Models for EEG-Based Identification of Pain Perception](https://mlsanity.com/paper/2508.11691/) (2025)
- [Flatness and Gradient Alignment Are Both Necessary: Spectral-Aware Gradient-Aligned Exploration for Multi-Distribution Learning](https://mlsanity.com/paper/2605.07914/) (2026)
- [HeartBeatAI: An Interpretable and Robust Deep Learning Framework for Multi-Label ECG Arrhythmia Detection](https://mlsanity.com/paper/2605.24588/) (2026)
- [Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations](https://mlsanity.com/paper/2608.09053/) (2026)

## Abstract (arXiv)

Despite their immense success in numerous fields, machine and deep learning systems have not yet been able to firmly establish themselves in mission-critical applications in healthcare. One of the main reasons lies in the fact that when models are presented with previously unseen, Out-of-Distribution samples, their performance deteriorates significantly. This is known as the Domain Generalization (DG) problem. Our objective in this work is to propose a benchmark for evaluating DG algorithms, in addition to introducing a novel architecture for tackling DG in biosignal classification. In this paper, we describe the Domain Generalization problem for biosignals, focusing on electrocardiograms (ECG) and electroencephalograms (EEG) and propose and implement an open-source biosignal DG evaluation benchmark. Furthermore, we adapt state-of-the-art DG algorithms from computer vision to the problem of 1D biosignal classification and evaluate their effectiveness. Finally, we also introduce a novel neural network architecture that leverages multi-layer representations for improved model generalizability. By implementing the above DG setup we are able to experimentally demonstrate the presence of the DG problem in ECG and EEG datasets. In addition, our proposed model demonstrates improved effectiveness compared to the baseline algorithms, exceeding the state-of-the-art in both datasets. Recognizing the significance of the distribution shift present in biosignal datasets, the presented benchmark aims at urging further research into the field of biomedical DG by simplifying the evaluation process of proposed algorithms. To our knowledge, this is the first attempt at developing an open-source framework for evaluating ECG and EEG DG algorithms.