Abstract
Adapting machine learning models to medical time series across different domains remains a challenge due to complex temporal dependencies and dynamic distribution shifts. Current approaches often focus on isolated feature representations, limiting their ability to fully capture the intricate temporal dynamics necessary for robust domain adaptation. In this work, we propose a novel framework leveraging multi-view contrastive learning to integrate temporal patterns, derivative-based dynamics, and frequency-domain features. Our method employs independent encoders and a hierarchical fusion mechanism to learn feature-invariant representations that are transferable across domains while preserving temporal coherence. Extensive experiments on diverse medical datasets, including electroencephalogram (EEG), electrocardiogram (ECG), and electromyography (EMG) demonstrate that our approach significantly outperforms state-of-the-art methods in transfer learning tasks. By advancing the robustness and generalizability of machine learning models, our framework offers a practical pathway for deploying reliable AI systems in diverse healthcare settings.
Abstract (translated)
将机器学习模型适应不同领域中的医疗时间序列数据仍然是一个挑战，原因在于复杂的时间依赖性和动态分布变化。目前的方法往往集中在孤立的特征表示上，这限制了它们捕捉稳健域适应所必需的复杂时间动态的能力。在本工作中，我们提出了一种利用多视角对比学习的新框架，以整合时间模式、基于导数的动力学和频域特征。我们的方法采用独立编码器和分层融合机制来学习跨领域可迁移且保持时间一致性的不变性表示。我们在包括脑电图（EEG）、心电图（ECG）和肌电图（EMG）在内的多样化医疗数据集上进行了广泛的实验，结果表明我们的方法在迁移学习任务中显著优于最先进的方法。通过增强机器学习模型的鲁棒性和泛化能力，我们的框架为在各种医疗环境中部署可靠的AI系统提供了实用路径。
URL
https://arxiv.org/abs/2506.22393
