  Multivariate biosignals are prevalent in many medical domains, such as
electroencephalography, polysomnography, and electrocardiography. Modeling
spatiotemporal dependencies in multivariate biosignals is challenging due to
(1) long-range temporal dependencies and (2) complex spatial correlations
between the electrodes. To address these challenges, we propose representing
multivariate biosignals as time-dependent graphs and introduce GraphS4mer, a
general graph neural network (GNN) architecture that improves performance on
biosignal classification tasks by modeling spatiotemporal dependencies in
biosignals. Specifically, (1) we leverage the Structured State Space
architecture, a state-of-the-art deep sequence model, to capture long-range
temporal dependencies in biosignals and (2) we propose a graph structure
learning layer in GraphS4mer to learn dynamically evolving graph structures in
the data. We evaluate our proposed model on three distinct biosignal
classification tasks and show that GraphS4mer consistently improves over
existing models, including (1) seizure detection from electroencephalographic
signals, outperforming a previous GNN with self-supervised pre-training by 3.1
points in AUROC; (2) sleep staging from polysomnographic signals, a 4.1 points
improvement in macro-F1 score compared to existing sleep staging models; and
(3) 12-lead electrocardiogram classification, outperforming previous
state-of-the-art models by 2.7 points in macro-F1 score.
