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# System prepended metadata

title: GraphSleepNet(2020)

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# GraphSleepNet(2020)
>[Source](https://www.ijcai.org/proceedings/2020/0184.pdf)

- ### Abstract
    - The model proposed GraphSleepNet learns the intrinsic connection between different EEG channels represented by an adjacency matric and passes it to Spatial Temporal Graph convolution network(ST-GCN).
    - Each channel is corresponds to a node and the connection between the channels as edges.
    - A limitation that other methods have is the input must be grid data and as brain regions are not in Euclidean space graph structures would be better.

- ### Model
    - ![](https://i.imgur.com/avSuXgQ.png)
    - ![](https://i.imgur.com/6Zeops1.png)

    - **Adaptive Sleep Graph Learning**
        - ![](https://i.imgur.com/3uwGReF.png)
        - It dynamically learns the hraph structure instead of having a fixed graph.
    - **Spatial-Temporal Graph COnvolution**
        - Spatial-temporal graph convolution is a combination of spatial graph convolution and temporal standard convolution, which is used to extract both spatial and temporal features.
        - Spacial convolution is based on spectral graph theory to extract spatial features, which uses Chebyshev extension to reduce complexity.
        - Temporal concolutions capture sleep transition rules and uses CNNs.
    - **Spatial-Temporal Attention**
        - In the spatial dimension, different regions have different effects which dynamically changes during sleep.
        - In the temporal dimension, there are correlations between different sleep stages.
        - These have attention mechanisms applied to extract valuable features.

- ### Evaluation
    - The MASS Dataset is used and is preprocessed with different bandpass ffilters for each channel.
    - A 31 fold cross validation is used to evaluate the performance of GraphSleepNet.
    - It has accuracy of 88.9 and N1 has F1 score of 60.3.


