论文标题
对生物信号的主题意识到的对比度学习
Subject-Aware Contrastive Learning for Biosignals
论文作者
论文摘要
生物信号的数据集，例如脑电图（EEG）和心电图（ECG），通常具有嘈杂的标签，受试者数量有限（<100）。为了应对这些挑战，我们提出了一种基于对比度学习的自我监督方法，以模拟生物信号，以减少对标记数据的依赖和较少的受试者的依赖。在这种有限标签和受试者的制度中，受试者间的变异性会对模型性能产生负面影响。因此，我们通过（1）特定于主体的对比损失引入了学科感知的学习，以及（2）对自我监督学习期间促进主题不变的对抗性训练。我们还开发了许多时间序列数据增强技术，可与生物信号的对比损失一起使用。我们的方法对具有不同任务的两个不同生物信号的公开数据集进行了评估：EEG解码和ECG异常检测。与完全监督的方法相比，使用自我指控学到的嵌入会产生竞争性分类结果。我们表明，主题不变会提高这些任务的表示质量，并观察到特定于主题的损失在用监督标签进行微调时会提高性能。
Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100). To handle these challenges, we propose a self-supervised approach based on contrastive learning to model biosignals with a reduced reliance on labeled data and with fewer subjects. In this regime of limited labels and subjects, intersubject variability negatively impacts model performance. Thus, we introduce subject-aware learning through (1) a subject-specific contrastive loss, and (2) an adversarial training to promote subject-invariance during the self-supervised learning. We also develop a number of time-series data augmentation techniques to be used with the contrastive loss for biosignals. Our method is evaluated on publicly available datasets of two different biosignals with different tasks: EEG decoding and ECG anomaly detection. The embeddings learned using self-supervision yield competitive classification results compared to entirely supervised methods. We show that subject-invariance improves representation quality for these tasks, and observe that subject-specific loss increases performance when fine-tuning with supervised labels.
