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, 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].

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.

• n_classes – Alias for n_outputs.

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]

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)

Forward pass.

Parameters:

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