Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2017, Article ID 4574079, 8 pages
https://doi.org/10.1155/2017/4574079
Research Article

A Comparison Study on Multidomain EEG Features for Sleep Stage Classification

1Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai, China
2Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
3Research Institute of Systems Control, Institute for Advanced Research and Education, Saga, Japan

Correspondence should be addressed to Bei Wang; nc.ude.tsuce@gnawieb

Received 10 July 2017; Revised 22 September 2017; Accepted 11 October 2017; Published 5 November 2017

Academic Editor: Pasi A. Karjalainen

Copyright © 2017 Yu Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Uchida, I. Feinberg, J. D. March, Y. Atsumi, and T. Maloney, “A comparison of period amplitude analysis and FFT power spectral analysis of all-night human sleep EEG,” Physiology & Behavior, vol. 67, no. 1, pp. 121–131, 1999. View at Publisher · View at Google Scholar · View at Scopus
  2. W. Shi, P. Shang, Y. Ma, S. Sun, and C.-H. Yeh, “A comparison study on stages of sleep: quantifying multiscale complexity using higher moments on coarse-graining,” Communications in Nonlinear Science and Numerical Simulation, vol. 44, pp. 292–303, 2017. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. A. R. Hassan and M. I. H. Bhuiyan, “An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting,” Neurocomputing, vol. 219, pp. 76–87, 2017. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Diykh and Y. Li, “Complex networks approach for EEG signal sleep stages classification,” Expert Systems with Applications, vol. 63, pp. 241–248, 2016. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Rechtschaffen and A. Kales, A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects, UCLA Brain Information Service/Brain Research Institute, Los Angeles, Calif, USA, 1968.
  6. Y. Tsuji, T. Kobayashi, M. Kousaka, H. kuwahara et al., “Proposed supplements and amendments to, A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects, The Rechtschaffen Kales,” Phychiatry Clinical Neuroscience, vol. 55, no. 3, pp. 305–310, 1968. View at Google Scholar
  7. Z. Liu, J. Sun, Y. Zhang, and P. Rolfe, “Sleep staging from the EEG signal using multi-domain feature extraction,” Biomedical Signal Processing and Control, vol. 30, pp. 86–97, 2016. View at Publisher · View at Google Scholar · View at Scopus
  8. B. Hjorth, “EEG analysis based on time domain properties,” Electroencephalography and Clinical Neurophysiology, vol. 29, no. 3, pp. 306–310, 1970. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Ahmadlou, H. Adeli, and A. Adeli, “New diagnostic EEG markers of the Alzheimer's disease using visibility graph,” Journal of Neural Transmission, vol. 117, no. 9, pp. 1099–1109, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. Q. Yuan, W. Zhou, S. Li, and D. Cai, “Epileptic EEG classification based on extreme learning machine and nonlinear features,” Epilepsy Research, vol. 96, no. 1-2, pp. 29–38, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. A. K. Maity, R. Pratihar, A. Mitra et al., “Multifractal detrended fluctuation analysis of alpha and theta EEG rhythms with musical stimuli,” Chaos, Solitons & Fractals, vol. 81, no. part A, pp. 52–67, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  12. G. Zhu, Y. Li, and P. P. Wen, “Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal,” IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 6, pp. 1813–1821, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Ronzhina, O. Janoušek, J. Kolářová, M. Nováková, P. Honzík, and I. Provazník, “Sleep scoring using artificial neural networks,” Sleep Medicine Reviews, vol. 16, no. 3, pp. 251–263, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. C.-S. Huang, C.-L. Lin, L.-W. Ko, S.-Y. Liu, T.-P. Su, and C.-T. Lin, “Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels,” Frontiers in Neuroscience, vol. 8, article no. 263, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. U. R. Acharya, V. K. Sudarshan, H. Adeli et al., “A novel depression diagnosis index using nonlinear features in EEG signals,” European Neurology, vol. 74, no. 1-2, pp. 79–83, 2016. View at Publisher · View at Google Scholar · View at Scopus
  16. F. Shayegh, S. Sadri, R. Amirfattahi, and K. Ansari-Asl, “A model-based method for computation of correlation dimension, Lyapunov exponents and synchronization from depth-EEG signals,” Computer Methods and Programs in Biomedicine, vol. 113, no. 1, pp. 323–337, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. L. A. Manilo and S. S. Volkova, “Recognition of the deep anesthesia stage from parameters of the approximated entropy of EEG signal,” Pattern Recognition and Image Analysis, vol. 23, no. 1, pp. 92–97, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. D. Easwaramoorthy and R. Uthayakumar, “Improved generalized fractal dimensions in the discrimination between Healthy and Epileptic EEG Signals,” Journal of Computational Science, vol. 2, no. 1, pp. 31–38, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. R. Yi, “Approximate entropy analysis of electroencephalogram,” Chinese Journal of Biomedical Engineering, vol. 20, no. 1, pp. 19–22, 2011. View at Google Scholar
  20. Q. Wei, Q. Liu, S.-Z. Fan et al., “Analysis of EEG via multivariate empirical mode decomposition for depth of anesthesia based on sample entropy,” Entropy, vol. 15, no. 9, pp. 3458–3470, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. E. Ferlazzo, N. Mammone, V. Cianci et al., “Permutation entropy of scalp EEG: A tool to investigate epilepsies. Suggestions from absence epilepsies.,” Clinical Neurophysiology, vol. 125, no. 1, pp. 13–20, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. K. Inoue, T. Tsujihata, K. Kumamaru, and S. Matsuoka, “Feature extraction of human sleep EEG based on a peak frequency analysis,” IFAC Proceedings Volumes, vol. 38, pp. 1059–1064, 2005. View at Google Scholar · View at Scopus
  23. B. Ahmed, A. Redissi, and R. Tafreshi, “An automatic sleep spindle detector based on wavelets and the teager energy operator,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2596–2599, IEEE, Minneapolis, Minn, USA, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. A. R. Hassan and M. I. H. Bhuiyan, “A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features,” Journal of Neuroscience Methods, vol. 271, pp. 107–118, 2016. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Zhang, J. Zou, M. Wang, L. Chen, C. Wang, and G. Wang, “Automatic detection of interictal epileptiform discharges based on time-series sequence merging method,” Neurocomputing, vol. 110, pp. 35–43, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. U. R. Acharya, O. Faust, N. Kannathal, T. Chua, and S. Laxminarayan, “Non-linear analysis of EEG signals at various sleep stages,” Computer Methods and Programs in Biomedicine, vol. 80, no. 1, pp. 37–45, 2005. View at Publisher · View at Google Scholar · View at Scopus