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A pilot project to create a robust sleep scorer using Convolutional Neural Networks with Long Short-Term Memory. It is a follow-up of my Master's Thesis: Automatic Sleep Stage Classification using Convolutional Neural Networks with Long Short-Term Memory

This package is a pilot project and still under development. There is no clinical validation and no support. The package is experimental at least. As of November 2017 development has ceased. Please do not use this package in clinical or research setup. The pretrained weights supplied at the moment are random snapshots of one of the trainings of the CCSHS50 dataset and are only for demonstrational purposes. The final goal is to have weights trained on different datasets, but I have not had time so far.

Sample Hypnogram


In this project a Convolutional Neural Network with Long Short-Term Memory is used for the detection of sleep stages. This approach has the advantage that it can automatically detect and extract features from the raw EEG, EMG and EOG signal (see here for example features that are learned by the network). The network architecture was trained and evaluated on several different public and private datasets to ensure a good generalizability (CCSHS, EDFx, EMSA). The network architecture can be found here.

Currently the classifier reaches the state-of-the-art of automatic sleep stage classification while obtaining a similar performance to a human scorer. Further results and details on the architecture are available in my Thesis

Classifier performance (5-fold cross validation)

Scores obtained training and testing within one dataset.

Data Set Agreement F1-score
CCSHS 89% 81%
EDFx 87% 80%
EMSA 87% 77%
- - -
General Inter Rater Reliability ~80-82% ?


The Classifier expects a PSG recording with at least one EEG, EMG and EOG present. Theoretically all data formats supported by MNE can be used, or any numpy data in the format [epochs, 3000, 3]. All data will be resampled to 100 Hz.

Documentation and command line instructions are coming soon. For now please see Quickstart.


The AutoSleepScorer is currently running with Python 3 using Keras with Tensorflow and has been tested on Windows 10. Due to Pythons multi-platform support it should run other OS as well. The easiest way to get started is using Anaconda, a Python package manager.

1. Install Anaconda

Download and install Anaconda with Python 3.6 64 bit from

If you already have a working Python 3.x environment you can skip this step.

2. Install Tensorflow

Open a command line and install tensorflow via pip install tensorflow

If you wish to have GPU support with a GeForce graphics card and have installed CUDA you can use pip install tensorflow-gpu. Running calculations on the GPU accelerates them significantly. Installation of CUDA can be a bit tricky and is described here:

3. Install AutoSleepScorer

Clone and install this repository via pip: pip install git+

If you get an error thatgit is not installed, you can install it using the command line conda install git


Open a python console for instance using Anaconda.

Minimal example

For quick classification

from sleepscorer import Scorer
file = "eeg_filename" #link to the EEG header or EEG file
scorer = Scorer([file], hypnograms=True)

Extended example

First we download a sample file from the EDFx database

from sleepscorer import tools
# download sample EEG file from the EDFx database'', 'sample-psg.edf')
# download corresponding hypnogram for comparrison of classification'', 'sample-psg.groundtruth.csv') 

Now we can start the Scorer using a list of EEG files. Instead of an EEG-filename we can also set advanced options using a SleepData object

# create a SleepData object 
from sleepscorer import Scorer, SleepData
file = SleepData('sample-psg.edf', start = 2880000, stop = 5400000, 
							  channels={'EEG':'EEG Fpz-Cz', 'EMG':'EMG submental', 
                              			'EOG':'EOG horizontal'}, preload=False)
# Create and run Scorer
scorer = Scorer([file], hypnograms=True, demo=True)
# this will only work if you have matplotlib set-up
tools.show_sample_hypnogram('sample-psg.groundtruth.csv', start=960, stop=1800)

The predictions will now be saved as sample-psg.edf.csv, where each row corresponds to one epoch (0=W, 1=S1, 2=S2, 3=SWS, 4=REM).

Extended using numpy arrays

If you want to handle the data loading yourself you can do so. Data needs to be sampled with 100 Hz. EEG and EOG are high-pass filtered with 0.15 Hz and the EMG has a high-pass filter of 10 Hz. Data needs to be in the format [epochs, 3000, 3] where the last dimension is EEG, EMG and EOG.

from sleepscorer import Classifier
data = ... # load your python array, preprocessed
assert(data.ndim==3 and data.shape[1:]==(3000,3))

clf = Classifier()
clf.download_weights()  # skip this if you already downloaded them.
preds = clf.predict(data, classes=True)

Appendix: Sleep Datasets

A short overview of useful sleep datasets

Abbreviation Study Name Recordings Condition
CCSHS Cleveland Children's Sleep and Health Study 515 health, young
ISRUC-SLEEP ISRUC-SLEEP Dataset 126 healthy + disordered
EDFx The Sleep-EDF Database 153 healthy
UCDDB University College Dublin Sleep Apnea Database 25 sleep apnoe
DREAMS The DREAMS Subjects Database 20 healthy
SDRC study on psychophysiological insomnia 60 healthy&insomnia patients
MGH sleep laboratory patients 1985 mixed
more see here...


An open-source sleep stage classification Python package





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