Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals
Source abstract
Abstract
Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.
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Research context
OpenAlex topics: EEG and Brain-Computer Interfaces · Sleep and Wakefulness Research · Sleep and Work-Related Fatigue
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa 9201192, Japan
Department of Computer and Informatics Engineering, Politeknik Negeri Jakarta, Depok 16425, Indonesia
Telkom of Computing, Telkom University, Bandung 40257, Indonesia
Computer Science, Lambung Mangkurat University, Banjarbaru 70714, Indonesia
Institute of Science and Engineering, Kanazawa University, Kanazawa 9201192, Japan
References (29)
Show source-provided references
1. Associations Between Sleep Duration Patterns and Behavioral/Cognitive Functioning at School Entry10.1093/sleep/30.9.1213 ↗
2. Sleep, Memory, and Plasticity10.1146/annurev.psych.56.091103.070307 ↗
3. Identification of Electroencephalographic Arousals in Multichannel Sleep Recordings10.1109/TBME.2010.2075930 ↗
4. Keenan, S.A. (2005). Handbook of Clinical Neurophysiology, An Overview of Polysomnography, Elsevier B.V.. Chapter 3.10.1016/S1567-4231(09)70028-0 ↗
5. Smith, H.R., Comella, C.L., and Hogl, B.L. (2012). Sleep Medicine, Cambridge University Press.
6. Kaniusas, E. Biomedical Signals and Sensors I, Springer.
7. Aboalayon, K.A.I., and Faezipour, M. (2014, January 8–10). Multi-Class SVM Based on Sleep Stage Identification Using EEG Signal. Proceedings of the 2014 IEEE Healthcare Innovation Conference (HIC), Piscataway, NJ, USA.10.1109/HIC.2014.7038904 ↗
8. Thorpy, M.J. (2001). The International Classification Of Sleep Disorders: Diagnostic and Coding Manual. Rev.Ed, One Westbrook Corporate Center, Suite 920.
9. Rechtschaffen, A., and Kales, A. (1968). A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects, National Government Publication.
10. Iber, C., and Medicine, A.A.o.S. (2007). The AASM Manual for The Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, American Academy of Sleep Medicine.
11. Sleep is of The Brain, by The Brain and For The Brain10.1038/nature04283 ↗
12. Boosting Slow Oscillations During Sleep Potentiates Memory10.1038/nature05278 ↗
13. Interobserver Agreement Among Sleep Scorers From Different Centers in a Large Dataset10.1093/sleep/23.7.1e ↗
14. Sleep Scoring Using Artificial Neural Networks10.1016/j.smrv.2011.06.003 ↗
15. Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From A Single-Channel EEG Signal10.1109/JBHI.2014.2303991 ↗
16. Knowledge-based Identification of Sleep Stages Based on Two Forehead Electroencephalogram Channels10.3389/fnins.2014.00263 ↗
17. Aboalayon, K.A.I., Faezipour, M., Almuhammadi, W.S., and Moslehpour, S. (2016). Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation. Entropy, 18.10.3390/e18090272 ↗
18. Braun, E.T., Silvera, T.L.T.D., Kozakevicius, A.D.J., Rodrigues, C.R., and Giovani, B. (2018). Sleep Stages Classification Using Spectral Based Statistical Moments as Features. Revista de Informática Teórica e Aplicada, 25.10.22456/2175-2745.74030 ↗
19. A Decision Support System for Automated Identification of Sleep Stages from Single-Channel EEG Signals10.1016/j.knosys.2017.05.005 ↗
20. Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models10.1109/TIM.2012.2187242 ↗
21. Nakamura, T., Adjei, T., Alqurashi, Y., Looney, D., Morrell, M.J., and Mandic, D.P. (2017, January 14–19). Complexity science for sleep stage classification from EEG. Proceedings of the International Joint Conference on Neural Networks, Anchorage, AK, USA.10.1109/IJCNN.2017.7966411 ↗
22. Yildirim, O., Baloglu, U.B., and Acharya, U.R. (2019). A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals. Int. J. Environ. Res. Public Health, 16.10.3390/ijerph16040599 ↗
23. Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains
24. Spatial EEG patterns, non-linear dynamics and perception: the neo-Sherringtonian view10.1016/0165-0173(85)90022-0 ↗
25. Nussbaumer, H.J. (1981). Fast Fourier Transform and Convolution Algorithms, Springer.10.1007/978-3-662-00551-4 ↗
26. Effective and Extensible Feature Extraction Method Using Genetic Algorithm-based Frequency-Domain Feature Search for Epileptic EEG Multiclassification10.1097/MD.0000000000006879 ↗
27. Sleep staging algorithm based on multichannel data adding and multifeature screening10.1016/j.cmpb.2019.105253 ↗
28. Timplalexis, C., Diamantaras, K., and Chouvarda, I. (2019, January 28–30). Classification of Sleep Stages for Healthy Subjects and Patients with Minor Sleep Disorders. Proceedings of the 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), Athens, Greece.10.1109/BIBE.2019.00068 ↗
29. SMOTE: Synthetic Minority Over-sampling Technique