Labeling of multivariate biomedical time series data is a laborious and
expensive process. Self-supervised contrastive learning alleviates the need for
large, labeled datasets through pretraining on unlabeled data. However, for
multivariate time series data the set of input channels often varies between
applications, and most existing work does not allow for transfer between
datasets with different sets of input channels. We propose learning one encoder
to operate on all input channels individually. We then use a message passing
neural network to extract a single representation across channels. We
demonstrate the potential of this method by pretraining our network on a
dataset with six EEG channels and finetuning on a dataset with two different
EEG channels. We compare networks with and without the message passing neural
network across different contrastive loss functions. We show that our method
combined with the TS2Vec loss outperforms all other methods in most settings.

通过在多通道生物医学时间序列数据上进行自我监督对比学习，使用消息传递神经网络提取跨通道的单一表示，并结合 TS2Vec 损失函数，我们的方法在大多数设置中优于其他方法。