# Classification of Sleep Stage with Biosignal Images Using Convolutional Neural Networks
## 7 Conclusions  
In this paper, a new sleep-stage classification method based on image classification is proposed. The idea is to use EEG and EOG images instead of conventional hand-crafted features in sleep-stage classification. This method is not necessary for feature extraction and selection, but provides a natural graphic interface to somnologists using CNN, which is an excellent algorithm for image recognition.  
The EEG and EOG signals in time and frequency domains of sleep epochs were converted to binary images, and these images were fed to the pre-trained CNN. The proposed method achieved a state-of-the-art performance (accuracy: 94%, weighted F1 score: 94%) on the public Sleep-EDFx dataset. It was demonstrated that using the CNN, it is possible to extract features from biosignals and learn similar patterns to those described by the AASM. The results showed that the classifier reaches outstanding results, as in other studies.  
For future work, we would verify the proposed method compared to other sleep datasets, conduct further optimization of parameters, and utilize pre-trained CNNs. Sleep-stage classifiers are still not used in the commercial field despite having higher accuracy. For sleep-stage classifiers to be commercialized, they should be validated with more datasets so that the classifiers can archive a level of confidence similar to that of somnologists. More research is needed for CNNs to be used in clinical practice.  
The code, trained models, and results are publicly available at [[https://github.com/psch125/sleep_classification](https://github.com/psch125/sleep_classification)]([https://github.com/psch125/sleep_classification](https://github.com/psch125/sleep_classification)) (accessed on 3 January 2022).  
Conceptualization, M.-J.J.; methodology, M.-J.J.; software, S.-C.P.; validation, S.-C.P.; formal analysis, M.-J.J. and S.-C.P.; investigation, M.-J.J.; resources, M.-J.J.; data curation, M.-J.J.; writing--original draft preparation, M.-J.J. and S.-C.P.; writing--review and editing, M.-J.J.; supervision, M.-J.J.; project administration, M.-J.J.; funding acquisition, M.-J.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We thank the anonymous reviewers for their careful reading and insightful suggestions to help improve the manuscript.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Dataset** & **Input** & **Method** & **Accuracy (\%)** \\ \hline [PERSON] [14] & Sleep-EDF *, MASS ** & EEG & CNN & 74.8, 77.9 \\ [PERSON] [12] & MASS & EOG & DNN & 81.4 \\ [PERSON] [27] & Sleep-EDFx, MASS & EEG & CNN + RNN & 82.0, 81.5 \\ [PERSON] [18] & Sleep-EDF, MASS & EEG, EOG & CNN & 76.8, 79.4 \\ [PERSON] [15] & Sleep-EDF & EEG, EOG & CNN & 84.0 \\ [PERSON] [17] & MASS & EEG, EOG, EMG & CNN & 79.9 \\ [PERSON] [20] & SHHS1 *** & EEG & CNN & 87.0 \\ [PERSON] [19] & Sleep-EDF & EEG, EOG & CNN & 82.3 \\ [PERSON] [16] & Sleep-EDFx & EEG, EOG & CNN & 92.3 \\ \hline
**This study** & **Sleep-EDFx** & **EEG, EOG** & **CNN** & **94.0** \\ \hline \hline \end{tabular}
* Sleep-EDF—subset version of Sleep-EDFx. ** MASS—Montreal Archive of Sleep Studies [35]. *** SHHS1—Sleep Heart Health Study [36, 37].  
\end{table}
Table 6: A comparison of sleep-stage classification methods.