ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2023

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-031-30678-5_27