---
# System prepended metadata

title: SalientSleepNet(2021)

---

# SalientSleepNet(2021)
>[source](https://arxiv.org/pdf/2105.13864.pdf)
- ### Abstract
    - The model proposed in the paper uses an U<sup>2</sup>structure stream to detect salientwaves.This is inspired by a similar structure used in computer vision.
    - A multiscale extraction modeule is used to capture sleep transition rules. It is made up of multiple dilated convolutions with different receptive fields for capturing multi-scale rules.
    - A multimodal attention module is used to capture information from different multimodal data.
    - It has lesser parameters compared to existing deep neural networks for sleep staging.
- ### Model
    - It uses U<sup>2</sup> structure to capture Salient waves in EEG and EOG modalities.
    - Multi-scale extraction module to learn multi-scale sleep transition rules.
    - Multimodal attention module to fuse attributes from EEG and EOG streams.
    - A segment-wise classifier along with a bottleneck layer to reduce computational cost.
    - ![](https://i.imgur.com/XAtbWX6.png)
    - **Two Stream U<sup>2</sup> structure**:
        - Sleep stages are classified using salient waves in EEG and EOG.
        - Current methods convert the raw signals into time0frequency images which may cause information loss.
        - A 2 stream U<sup>2</sup> structure is used for EEG and EOG signals to learn distinctive features.
        - Each structure is an encoder decoder structure with multiple nested U units.
        - The residual connection reduces degradation problem in deep networks.
        - The encoder has 5 U-unit and decoder has 4 U-unit.
    - **Multiscale extraction module(MSE):**
        - ![](https://i.imgur.com/9WoFq3h.png)
        - To learn sleep transition rules effective MSE used dilated convolutions with dofferent dilation rates.
        - This model uses 4 dilated convolutions from same input feature map.
        - The results of diluted convolutions are concatenated and passed to bottleneck layer.
        - Bottleneck layer reducles channels such a way C<sub>out</sub> = C<sub>in</sub>/downsampling rate.
    - **Multimodal Attention Module(MMA):**
        - ![](https://i.imgur.com/z9bkWlz.png)
        - Different modalities have distinctive features which contribute to classifying sleep stages which arent accounted for in existing models.
        - The MMA component fuses the feature maps from 2 streams and channel wise attention in implicit manner.
        - The fusion method is as:
        - ![](https://i.imgur.com/q2dmw6x.png)
        - This is fed into a channel wise attention block and mutliplied elementwise with X<sub>fuse</sub>.
    - Segment-wise classfication:
        - The salient object detection models in cv are pixel wise classifiers which cannot be directly applied to physiological signals.
        - Hence a segment-wise classifier which maps pixel-wise feature maps to segment wise predict labels sequence.
        - ![](https://i.imgur.com/w5Xssf7.png)

- ### Evaluation
    - The sleep edf dataset is used for evaluation.
    - N1 accuracy(F1 score) is better than other base-line models by 4%(56.2 others are 52ish).
    - A few mized models can achieve better accuracy but are difficult to tune and optimize and do not use salient wave features.




        
    
 
