This project forked from kedarps/deepsleepnet
Implementation of DeepSleepNet for classifying EEG signals into one of six sleep stages based on Supratak et. al 2017
This is an implementation of DeepSleepNet as described in Supratak et. al 2017 using Keras+Tensorflow.
The top part of the neural network employs two parallel deep convolutional networks, one on left tries to learn fine temporal features, whereas the one on the right tries to learn coarse temporal features. Features learnt in ultimate layers is concatenated and fed into two Bidirectional LSTM layers Supratak et. al 2017.
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