Summary of Hierarchical Transformer For Electrocardiogram Diagnosis, by Xiaoya Tang et al.
Hierarchical Transformer for Electrocardiogram Diagnosis
by Xiaoya Tang, Jake Berquist, Benjamin A. Steinberg, Tolga Tasdizen
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel hierarchical transformer architecture for ECG signal analysis, which segments the model into multiple stages by assessing spatial size embeddings, eliminating the need for downsampling strategies or complex attention designs. The classification token aggregates information across feature scales, facilitating interactions between different stages of the transformer. The approach uses depth-wise convolutions in a six-layer convolutional encoder to preserve relationships between ECG leads and an attention gate mechanism learns associations among leads prior to classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how transformers can be adapted for ECG signal analysis, which is important because it could help doctors better understand heart rhythms. The new architecture uses multiple stages of the transformer model to look at different parts of the ECG signal, and it’s flexible enough to work with different input sizes and embedding networks. |
Keywords
» Artificial intelligence » Attention » Classification » Embedding » Encoder » Token » Transformer
