Web: http://arxiv.org/abs/2206.07656

June 16, 2022, 1:11 a.m. | Sahar Soltanieh, Ali Etemad, Javad Hashemi

cs.LG updates on arXiv.org arxiv.org

This paper systematically investigates the effectiveness of various
augmentations for contrastive self-supervised learning of electrocardiogram
(ECG) signals and identifies the best parameters. The baseline of our proposed
self-supervised framework consists of two main parts: the contrastive learning
and the downstream task. In the first stage, we train an encoder using a number
of augmentations to extract generalizable ECG signal representations. We then
freeze the encoder and finetune a few linear layers with different amounts of
labelled data for downstream arrhythmia …

analysis arxiv learning representation representation learning

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