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.

翻译：尽管机器学习和深度学习系统在众多领域取得了巨大成功，但在医疗健康等关键任务应用中尚未能稳固立足。主要原因在于，当模型面对前所未见的分布外样本时，其性能会显著下降，这被称为域泛化问题。本研究旨在提出一个用于评估域泛化算法的基准，同时引入一种新型架构以解决生物信号分类中的域泛化挑战。本文描述了面向生物信号的域泛化问题，聚焦于心电图和脑电图，并提出并实现了一个开源生物信号域泛化评估基准。此外，我们将计算机视觉领域的前沿域泛化算法适配至一维生物信号分类问题，并评估其有效性。最后，我们提出了一种利用多层表示提升模型泛化能力的新型神经网络架构。通过实施上述域泛化设置，我们能够通过实验证明心电与脑电数据集中存在域泛化问题。同时，相较于基线算法，我们提出的模型在两类数据集上均展现出更优效果，超越了当前最优方法。鉴于生物信号数据集中分布偏移的重要性，本基准旨在通过简化算法评估流程，推动生物医学域泛化领域的进一步研究。据我们所知，这是首个面向心电与脑电域泛化算法评估的开源框架开发尝试。