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 was a pilot project and is no longer being developed (as of 2017). There is no clinical validation and no support
The package is experimental at least. 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, and probably will never have.
As the project continues to get attention, I encourage you to take a look at newer project instead of my project, especially
- U-Sleep and U-Time
- it performs amazingly well, much much better than anything I've seen before! Seal of approval from my side :)
- YASA not quite as good as U-Sleep, but open source and with accessible models.
- TinySleepNet much better theoretical ground work than AutoSleepScorer, and using newer paradigms
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.
Download and install Anaconda with Python 3.6 64 bit from https://www.anaconda.com/download/#download
If you already have a working Python 3.x environment you can skip this step.
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: https://www.tensorflow.org/install/
Clone and install this repository via pip:
pip install git+https://github.com/skjerns/AutoSleepScorer
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.
For quick classification
from sleepscorer import Scorer
file = "eeg_filename" #link to the EEG header or EEG file
scorer = Scorer([file], hypnograms=True)
scorer.run()First we download a sample file from the EDFx database
from sleepscorer import tools
# download sample EEG file from the EDFx database
tools.download('https://physionet.org/physiobank/database/sleep-edfx/sleep-cassette/SC4001E0-PSG.edf', 'sample-psg.edf')
# download corresponding hypnogram for comparrison of classification
tools.download('https://pastebin.com/raw/jbzz16wP', '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)
scorer.run()
# 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).
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.
clf.load_cnn_model('./weights/cnn.hdf5')
clf.load_rnn_model('./weights/rnn.hdf5)
preds = clf.predict(data, classes=True)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... |