Recent algorithmic advances in electrocardiogram (ECG) classification are largely contributed to deep learning. However, these methods are still based on a relatively straightforward application of deep neural network...
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ISBN:
(数字)9781728173825
ISBN:
(纸本)9781728111056
Recent algorithmic advances in electrocardiogram (ECG) classification are largely contributed to deep learning. However, these methods are still based on a relatively straightforward application of deep neural networks (DNNs), which leaves incredible room for improvement. In this paper, as part of the PhysioNet / Computing in Cardiology Challenge 2020, we developed an 18-layer residual convolutional neural network to classify clinical cardiac abnormalities from 12-lead ECG recordings. We focused on examining a collection of data pre-processing, model architecture, training, and post-training procedure refinements for DNN-based ECG classification. We showed that by combining these refinements, we can improve the classification performance significantly. Our team, DSAIL_SNU, obtained a 0.695 challenge score using 10-fold cross-validation, and a 0.420 challenge score on the full test data, placing us 6th in the official ranking.
Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling. In these studies, models were pre-...
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