ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification

TL;DR

ShapeWordNet is an interpretable neural network that uses shapelet-based sequence discretization and contrastive learning to improve physiological signal classification, addressing data complexity and label sparsity.

Contribution

It introduces a novel shapelet-based sequence discretization and multi-scale contrastive learning framework tailored for physiological signals, enhancing interpretability and classification performance.

Findings

Outperforms four categories of state-of-the-art methods

Effectively preserves local patterns and global context in signals

Demonstrates good interpretability through visualization analysis

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.