ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification

Understand

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.