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+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4060151.svg)](https://doi.org/10.5281/zenodo.4060151)
+
+
+# EEG_classification
+Description of the approach : https://towardsdatascience.com/sleep-stage-classification-from-single-channel-eeg-using-convolutional-neural-networks-5c710d92d38e
+
+
+Sleep Stage Classification from Single Channel EEG using Convolutional Neural
+Networks
+
+*****
+
+<span class="figcaption_hack">Photo by [Paul
+M](https://unsplash.com/photos/7i9yLoUgoP8?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)
+on
+[Unsplash](https://unsplash.com/search/photos/owl?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)</span>
+
+Quality Sleep is an important part of a healthy lifestyle as lack of it can
+cause a list of
+[issues](https://www.webmd.com/sleep-disorders/features/10-results-sleep-loss#1)
+like a higher risk of cancer and chronic fatigue. This means that having the
+tools to automatically and easily monitor sleep can be powerful to help people
+sleep better.<br> Doctors use a recording of a signal called EEG which measures
+the electrical activity of the brain using an electrode to understand sleep
+stages of a patient and make a diagnosis about the quality if their sleep.
+
+In this post we will train a neural network to do the sleep stage classification
+automatically from EEGs.
+
+### **Data**
+
+In our input we have a sequence of 30s epochs of EEG where each epoch has a
+label [{“W”, “N1”, “N2”, “N3”,
+“REM”}](https://en.wikipedia.org/wiki/Sleep_cycle).
+
+<span class="figcaption_hack">Fig 1 : EEG Epoch</span>
+
+<span class="figcaption_hack">Fig 2 : Sleep stages through the night</span>
+
+This post is based on a publicly available EEG Sleep data (
+[Sleep-EDF](https://www.physionet.org/physiobank/database/sleep-edfx/) ) that
+was done on 20 subject, 19 of which have 2 full nights of sleep. We use the
+pre-processing scripts available in this
+[repo](https://github.com/akaraspt/deepsleepnet) and split the train/test so
+that no study subject is in both at the same time.
+
+The general objective is to go from a 1D sequence like in fig 1 and predict the
+output hypnogram like in fig 2.
+
+### Model Description
+
+Recent approaches [[1]](https://arxiv.org/pdf/1703.04046.pdf) use a sub-model
+that encodes each epoch into a 1D vector of fixed size and then a second
+sequential sub-model that maps each epoch’s vector into a class from [{“W”,
+“N1”, “N2”, “N3”, “REM”}](https://en.wikipedia.org/wiki/Sleep_cycle).
+
+Here we use a 1D CNN to encode each Epoch and then another 1D CNN or LSTM that
+labels the sequence of epochs to create the final
+[hypnogram](https://en.wikipedia.org/wiki/Hypnogram). This allows the prediction
+for an epoch to take into account the context.
+
+<span class="figcaption_hack">Sub-model 1 : Epoch encoder</span>
+
+<span class="figcaption_hack">Sub-model 2 : Sequential model for epoch classification</span>
+
+The full model takes as input the sequence of EEG epochs ( 30 seconds each)
+where the sub-model 1 is applied to each epoch using the TimeDistributed Layer
+of [Keras](https://keras.io/) which produces a sequence of vectors. The sequence
+of vectors is then fed into a another sub-model like an LSTM or a CNN that
+produces the sequence of output labels.<br> We also use a linear Chain
+[CRF](https://en.wikipedia.org/wiki/Conditional_random_field) for one of the
+models and show that it can improve the performance.
+
+### Training Procedure
+
+The full model is trained end-to-end from scratch using Adam optimizer with an
+initial learning rate of 1e⁻³ that is reduced each time the validation accuracy
+plateaus using the ReduceLROnPlateau Keras Callbacks.
+
+<span class="figcaption_hack">Accuracy Training curves</span>
+
+### Results
+
+We compare 3 different models :
+
+* CNN-CNN : This ones used a 1D CNN for the epoch encoding and then another 1D CNN
+for the sequence labeling.
+* CNN-CNN-CRF : This model used a 1D CNN for the epoch encoding and then a 1D
+CNN-CRF for the sequence labeling.
+* CNN-LSTM : This ones used a 1D CNN for the epoch encoding and then an LSTM for
+the sequence labeling.
+
+We evaluate each model on an independent test set and get the following results
+:
+
+* CNN-CNN : F1 = 0.81, ACCURACY = 0.87
+* CNN-CNN-CRF : F1 = 0.82, ACCURACY =0.89
+* CNN-LSTM : F1 = 0.71, ACCURACY = 0.76
+
+The CNN-CNN-CRF outperforms the two other models because the CRF helps learn the
+transition probabilities between classes. The LSTM based model does not work as
+well because it is most sensitive to hyper-parameters like the optimizer and the
+batch size and requires extensive tuning to perform well.
+
+<span class="figcaption_hack">Ground Truth Hypnogram</span>
+
+<span class="figcaption_hack">Predicted Hypnogram using CNN-CNN-CRF</span>
+
+Source code available here :
+[https://github.com/CVxTz/EEG_classification](https://github.com/CVxTz/EEG_classification)
+
+I look forward to your suggestions and feedback.
+
+[[1] DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw
+Single-Channel EEG](https://arxiv.org/pdf/1703.04046.pdf)
+
+How to cite:
+```
+@software{mansar_youness_2020_4060151,
+  author       = {Mansar Youness},
+  title        = {CVxTz/EEG\_classification: v1.0},
+  month        = sep,
+  year         = 2020,
+  publisher    = {Zenodo},
+  version      = {v1.0},
+  doi          = {10.5281/zenodo.4060151},
+  url          = {https://doi.org/10.5281/zenodo.4060151}
+}
+```