ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification

Wenqiang He, Mingyue Cheng, Qi Liu, Zhi Li

Abstract:

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.