  Physiological signals are high-dimensional time series of great practical
values in medical and healthcare applications. However, previous works on its
classification fail to obtain promising results due to the intractable data
characteristics and the severe label sparsity issues. In this paper, we try to
address these challenges by proposing a more effective and interpretable scheme
tailored for the physiological signal classification task. Specifically, we
exploit the time series shapelets to extract prominent local patterns and
perform interpretable sequence discretization to distill the whole-series
information. By doing so, the long and continuous raw signals are compressed
into short and discrete token sequences, where both local patterns and global
contexts are well preserved. Moreover, to alleviate the label sparsity issue, a
multi-scale transformation strategy is adaptively designed to augment data and
a cross-scale contrastive learning mechanism is accordingly devised to guide
the model training. We name our method as ShapeWordNet and conduct extensive
experiments on three real-world datasets to investigate its effectiveness.
Comparative results show that our proposed scheme remarkably outperforms four
categories of cutting-edge approaches. Visualization analysis further witnesses
the good interpretability of the sequence discretization idea based on
shapelets.
