  We train an enhanced deep convolutional neural network in order to identify
eight cardiac abnormalities from the standard 12-lead electrocardiograms (ECGs)
using the dataset of 14000 ECGs. Instead of straightforwardly applying an
end-to-end deep learning approach, we find that deep convolutional neural
networks enhanced with sophisticated hand crafted features show advantages in
reducing generalization errors. Additionally, data preprocessing and
augmentation are essential since the distribution of eight cardiac
abnormalities are highly biased in the given dataset. Our approach achieves
promising generalization performance in the First China ECG Intelligent
Competition; an empirical evaluation is also provided to validate the efficacy
of our design on the competition ECG dataset.
