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- W4308273444 abstract "In recent years, self-supervised methods have been widely used in representation learning for electrocardiogram (ECG), but most of the existing methods are based on contrastive learning. Contrastive learning methods usually rely on a large number of negative sample pairs and data augmentation. In this paper, we propose a masked autoencoder-based ECG representation learning model. Our approach is to mask the original ECG signal with a high ratio and then use the autoencoder to reconstruct the original ECG signal. To obtain better ECG features, our model not only extracts local features of ECG using multi-scale convolution, but also global features of ECG using transformer. Our model first pre-trains on the ECG datasets and then fine-tunes on each ECG classification task. Experimental results show that our model outperforms the extant SOTA models for self-supervised learning." @default.
- W4308273444 created "2022-11-10" @default.
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- W4308273444 date "2022-10-14" @default.
- W4308273444 modified "2023-09-23" @default.
- W4308273444 title "Masked Autoencoder for ECG Representation Learning" @default.
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- W4308273444 doi "https://doi.org/10.1109/icist55546.2022.9926900" @default.
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