  We propose a recurrent neural network classifier to detect pathologies in
12-lead ECG signals and train and validate the classifier with the Chinese
physiological signal challenge dataset (http://www.icbeb.org/Challenge.html).
The recurrent neural network consists of two bi-directional LSTM layers and can
train on arbitrary-length ECG signals. Our best trained model achieved an
average F1 score of 74.15% on the validation set.
  Keywords: ECG classification, Deep learning, RNN, Bi-directional LSTM, QRS
detection.
