Classification of 12-Lead ECG Signals with Bi-directional LSTM Network
This work addresses ECG classification for medical diagnosis, but it is incremental as it applies an existing deep learning method to a specific dataset.
The paper tackled the problem of detecting pathologies in 12-lead ECG signals using a bi-directional LSTM network, achieving an average F1 score of 74.15% on a validation set.
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.