DeepSleepNet

DeepSleepNet from Supratak et al (2017) [Supratak2017].

Architecture-only repository. Documents the braindecode.models.DeepSleepNet class. No pretrained weights are distributed here. Instantiate the model and train it on your own data.

Quick start

pip install braindecode

from braindecode.models import DeepSleepNet

model = DeepSleepNet(
    n_chans=2,
    sfreq=100,
    input_window_seconds=30.0,
    n_outputs=5,
)

The signal-shape arguments above are illustrative defaults — adjust to match your recording.

Documentation

• Full API reference: https://braindecode.org/stable/generated/braindecode.models.DeepSleepNet.html
• Interactive browser (live instantiation, parameter counts): https://huggingface.co/spaces/braindecode/model-explorer
• Source on GitHub: https://github.com/braindecode/braindecode/blob/master/braindecode/models/deepsleepnet.py#L12

Architecture

Parameters

Parameter	Type	Description
activation_large	type[nn.Module], default=nn.ELU	Activation class for the large-filter CNN path.
activation_small	type[nn.Module], default=nn.ReLU	Activation class for the small-filter CNN path.
return_feats	bool, default=False	If True, return features before the final linear layer.
drop_prob	float, default=0.5	Dropout probability applied throughout the network.
bilstm_hidden_size	int, default=512	Hidden size of the BiLSTM. The residual FC output dimension is 2 * bilstm_hidden_size to match the concatenated directions.
bilstm_num_layers	int, default=2	Number of stacked BiLSTM layers.
small_n_filters_1	int, default=64	First-conv output channels for the small-filter path.
small_n_filters_2	int, default=128	Deep-conv (conv2--conv4) output channels for the small-filter path.
small_first_kernel_size	int, default=50	First-conv kernel size for the small path (paper: Fs/2).
small_first_stride	int, default=6	First-conv stride for the small path (paper: Fs/16).
small_first_padding	int, default=22	First-conv padding for the small path.
small_pool1_kernel_size	int, default=8	First max-pool kernel for the small path.
small_pool1_stride	int, default=8	First max-pool stride for the small path.
small_pool1_padding	int, default=2	First max-pool padding for the small path.
small_deep_kernel_size	int, default=8	Deep-conv kernel size for the small path.
small_pool2_kernel_size	int, default=4	Second max-pool kernel for the small path.
small_pool2_stride	int, default=4	Second max-pool stride for the small path.
small_pool2_padding	int, default=1	Second max-pool padding for the small path.
large_n_filters_1	int, default=64	First-conv output channels for the large-filter path.
large_n_filters_2	int, default=128	Deep-conv (conv2--conv4) output channels for the large-filter path.
large_first_kernel_size	int, default=400	First-conv kernel size for the large path (paper: 4*Fs).
large_first_stride	int, default=50	First-conv stride for the large path (paper: Fs/2).
large_first_padding	int, default=175	First-conv padding for the large path.
large_pool1_kernel_size	int, default=4	First max-pool kernel for the large path.
large_pool1_stride	int, default=4	First max-pool stride for the large path.
large_pool1_padding	int, default=0	First max-pool padding for the large path.
large_deep_kernel_size	int, default=6	Deep-conv kernel size for the large path.
large_pool2_kernel_size	int, default=2	Second max-pool kernel for the large path.
large_pool2_stride	int, default=2	Second max-pool stride for the large path.
large_pool2_padding	int, default=1	Second max-pool padding for the large path.

References

1. 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.

Citation

Cite the original architecture paper (see References above) and braindecode:

@article{aristimunha2025braindecode,
  title   = {Braindecode: a deep learning library for raw electrophysiological data},
  author  = {Aristimunha, Bruno and others},
  journal = {Zenodo},
  year    = {2025},
  doi     = {10.5281/zenodo.17699192},
}

License

BSD-3-Clause for the model code (matching braindecode). Pretraining-derived weights, if you fine-tune from a checkpoint, inherit the licence of that checkpoint and its training corpus.