akaraspt/deepsleepnet
A CNN-LSTM deep learning model for automatically scoring sleep stages from raw single-channel EEG signals.
Not currently ranked — collecting fresh signals.
star history
DeepSleepNet is a deep learning model that classifies sleep stages from raw single-channel EEG recordings. It uses a convolutional neural network for feature extraction followed by a long short-term memory network for sequence modeling. The model was developed for sleep stage scoring research in healthcare applications and achieves competitive performance on standard datasets like MASS and Sleep-EDF. The architecture learned interpretable temporal patterns corresponding to sleep onset transitions.
Frequently asked
- What is akaraspt/deepsleepnet?
- A CNN-LSTM deep learning model for automatically scoring sleep stages from raw single-channel EEG signals.
- Is deepsleepnet open source?
- Yes — akaraspt/deepsleepnet is open source, released under the Apache-2.0 license.
- What language is deepsleepnet written in?
- akaraspt/deepsleepnet is primarily written in Python.
- How popular is deepsleepnet?
- akaraspt/deepsleepnet has 487 stars on GitHub.
- Where can I find deepsleepnet?
- akaraspt/deepsleepnet is on GitHub at https://github.com/akaraspt/deepsleepnet.