Low-dimensional Denoising Embedding Transformer for ECG Classification
2021
Online
report
The transformer based model (e.g., FusingTF) has been employed recently for Electrocardiogram (ECG) signal classification. However, the high-dimensional embedding obtained via 1-D convolution and positional encoding can lead to the loss of the signal's own temporal information and a large amount of training parameters. In this paper, we propose a new method for ECG classification, called low-dimensional denoising embedding transformer (LDTF), which contains two components, i.e., low-dimensional denoising embedding (LDE) and transformer learning. In the LDE component, a low-dimensional representation of the signal is obtained in the time-frequency domain while preserving its own temporal information. And with the low dimensional embedding, the transformer learning is then used to obtain a deeper and narrower structure with fewer training parameters than that of the FusingTF. Experiments conducted on the MIT-BIH dataset demonstrates the effectiveness and the superior performance of our proposed method, as compared with state-of-the-art methods.
Comment: To appear at ICASSP 2021
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Low-dimensional Denoising Embedding Transformer for ECG Classification
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Autor/in / Beteiligte Person: | Guan, Jian ; Wang, Wenbo ; Feng, Pengming ; Wang, Xinxin ; Wang, Wenwu |
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Veröffentlichung: | 2021 |
Medientyp: | report |
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