- class braindecode.models.DeepSleepNet(n_outputs=5, return_feats=False, n_chans=None, chs_info=None, n_times=None, input_window_seconds=None, sfreq=None, n_classes=None)#
Sleep staging architecture from Supratak et al 2017.
Convolutional neural network and bidirectional-Long Short-Term for single channels sleep staging described in [Supratak2017].
n_outputs (int) – Number of outputs of the model. This is the number of classes in the case of classification.
return_feats (bool) – If True, return the features, i.e. the output of the feature extractor (before the final linear layer). If False, pass the features through the final linear layer.
n_chans (int) – Number of EEG channels.
n_times (int) – Number of time samples of the input window.
input_window_seconds (float) – Length of the input window in seconds.
sfreq (float) – Sampling frequency of the EEG recordings.
n_classes – Alias for n_outputs.
If some input signal-related parameters are not specified, there will be an attempt to infer them from the other parameters.
Supratak, A., Dong, H., Wu, C., & Guo, Y. (2017). DeepSleepNet: A model for automatic sleep stage scoring based on raw single-channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(11), 1998-2008.