braindecode.models.DeepSleepNet#

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, activation_large: ~torch.nn.modules.module.Module = <class 'torch.nn.modules.activation.ELU'>, activation_small: ~torch.nn.modules.module.Module = <class 'torch.nn.modules.activation.ReLU'>, drop_prob: float = 0.5)[source]#

Sleep staging architecture from Supratak et al. (2017) [Supratak2017].

DeepSleepNet Architecture

Convolutional neural network and bidirectional-Long Short-Term for single channels sleep staging described in [Supratak2017].

Parameters:

  • 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.

  • chs_info (list of dict) – Information about each individual EEG channel. This should be filled with info["chs"]. Refer to mne.Info for more details.

  • 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.

  • activation_large (nn.Module, default=nn.ELU) – Activation function class to apply. Should be a PyTorch activation module class like nn.ReLU or nn.ELU. Default is nn.ELU.

  • activation_small (nn.Module, default=nn.ReLU) – Activation function class to apply. Should be a PyTorch activation module class like nn.ReLU or nn.ELU. Default is nn.ReLU.

  • drop_prob (float, default=0.5) – The dropout rate for regularization. Values should be between 0 and 1.

Raises:

  • ValueError – If some input signal-related parameters are not specified: and can not be inferred.

  • FutureWarning – If add_log_softmax is True, since LogSoftmax final layer: will be removed in the future.

Notes

If some input signal-related parameters are not specified, there will be an attempt to infer them from the other parameters.

References

[Supratak2017] (1,2)

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.

Methods

forward(x)[source]#

Forward pass.

Parameters:

x (torch.Tensor) – Batch of EEG windows of shape (batch_size, n_channels, n_times).