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# System prepended metadata

title: XSleepNet (2021)

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# XSleepNet (2021)
>[Source](https://arxiv.org/pdf/2007.05492.pdf)

- ### Abstract
    - XSleepNet uses multiview inputs and takes in both raw signals and time frequency images.
    - It learns jointly on these inputs complementarily.
- ### Model
    - The raw signal network is large and based on CNN, the time frequency signal network is compact and uses RNN.
    - They are trained such that learning on stream that is generalising is accepted and overfitting one is discouraged.
    - ![](https://i.imgur.com/SKMMy9y.png)
    - Architecture used bidirectional LSTMs.
    - It has 3 softmaz outputs, 2 are view specific and 3rd is joint output.
    - Loss weights are weighted on ration of generalization and overfitting.
    - w = (1/z) * (G/O<sup>2</sup>), Z is normalization factor.
    - Based on two approximations used for G and O, XsleepNet 1 and XsleepNet 2 are architectures used which employ loss and tangent curves respectively.

- ### Evaluation
    - Done of 5 datasets sleep-edf20,sleep-edf78,MASS,SHHS,PhysioNet2018.
    - Has an accuracy about 80-85 in all datasets with XsleepNet2 performing better.





