# Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals
## 4 Discussions and Conclusions  
In order to improve the performance of sleep stage classification, previous work has mainly focused on the following points:  
* More effective methods of feature extraction from the original EEG signal (e.g., wavelet transform)
* Application of filters (e.g., band-pass filter) and noise reduction algorithms
* Identification of better classifier algorithms (e.g., random forest, adaptive boosting, and convolutional neural network)
* Improvement of class imbalance by under- and/or over-sampling (e.g., SMOTE)  
In contrast, we have demonstrated in this paper that fully utilizing thousands of FFT features extracted from single- and multi-channel EEG signals in combination with simple feature selection is an effective means of improving the performance of automated sleep stage classification. In our experiment on 6- to 2-class classification against the Sleep-EDF dataset, our method outperformed other recent and advanced methods.  
Additionally, we demonstrated the result of application of our method to the classification of the recording included in the latest version of Sleep-EDF database. We clearly showed that accuracy in classifying a recording is highly influenced by the degree of class imbalance.  
The differences between the amounts of the majority class and the minority class in the datasets leads to an imbalanced dataset. In other words, balanced class distributions are essential in supervised learning as standard classification. One of the methods for solving this problem is by doing oversampling and it aims to achieve a balanced class distribution by creating an artificial data. SMOTE (synthetic minority over-sampling technique) is an over-sampling method that is typically used to balance an imbalanced data as a part of machine learning. New instances are created as minority class instances from minority class neighbors that performed like the original instances of the minority class [29]. Related to sleep stage classification, our experimental results suggested that by combining our method with under- and/or over-sampling methods like SMOTE, we may achieve better classification performance of the recordings in the latest Sleep-EDF database.  
One of the disadvantages in our method is the intensive computational requirements in memory and processor. However, it also means that if the available resource of computing is rich, its performance can be further improved. In addition to the analysis of relationship between the number of features and performance, we need to conduct future work on the effectiveness of our method in other datasets.  
Conceptualization, M.K.D. and K.S.; formal analysis, B.P., K.R.M., and F.I.; funding acquisition, K.S.; investigation, M.K.D.; methodology, K.S.; software, N.G.N., M.R.F., and M.K.; supervision, K.S.; writing--original draft, M.K.D.; writing--review and editing, B.P., K.R.M., F.I., and K.S. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by JSPS KAKENHI Grant Number JP18K11525 and Kanazawa University CHOZEN project.  
The first and second authors would like to gratefully acknowledge the BUDI-LN scholarship from Indonesia Endowment Fund for Education (LPDP), Ministry of Education and Culture Republic of Indonesia (KEMENDIKBUD), and Ministry of Research and Technology of Republic Indonesia (KEMENRISTEK). In this research, the super-computing resource was provided by Human Genome Center, the Institute of Medical Science, the University of Tokyo. Additional computation time was provided by the super computer system in Research Organization of Information and Systems (ROIS), National Institute of Genetics (NIG).  
The authors declare no conflict of interest