Abstract
A novel approach for detecting spindles from sleep EEGs (electroencephalograph) automatically is presented in this paper. Empirical mode decomposition (EMD) is employed to decompose a sleep EEG, which are usually typical nonlinear and non-stationary data, into a finite number of intrinsic mode functions (IMF). Based on these IMFs, the Hilbert spectrum of the EEG can be calculated easily and provides a high resolution time-frequency presentation. An algorithm is developed to detect spindles from a sleep EEG accurately, experiments of which show encouraging detection results.
This work was supported by NSFC (Nos. 60475042, 60133020), the National Key Basic Research Project of China (No.2004CB318000) and the Scientific and Technological Planning Project of Guangzhou city.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Hongzhi An and Min Chen. Non-linear Time series Analysis. Shanghai Science & Technology Press, China, 1998.
J. Doman, C. Detka, and T. Hoffman et al. Automating the sleep laboratory: Implementation and validation of digital recording and analysis. International Journal of Biomedical Computing, 38(3):277–290, 1995.
Fei Huang and Chongxun Zheng. Automated recognition of spindles in sleep electroencephalogram base on time-frequency analysis. Journal of Xi’An Jiao Tong University, 36(2):218–220, 2002.
N. E. Huang, Z. Shen, and S. R. Long et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London, A(454):903–995, 1998.
M. J. Korenberg. A robust orthogonal algorithm for system identification and time-series analysis. Biological Cybernetics, 60:267–276, 1989.
Leukel. Essential of Physiological Psychology. The CV Company, USA, 1978.
Jianping Liu, Shiyong Yang, and Chongxun Zheng. High resolution time-frequency analysis method for extracting the sleep spindles. Journal of Biomedical Engineering, 17(1):50–55, 2000.
N. Pradhan and P. K. Sadasivan. The nature of dominant lyapunov exponent and attractor dimension curve of eeg in sleep. Computers in Biology and Medicine, 26(5):419–428, 1996.
J. Pricipe, S. K. Gala, and T. G. Chang. Sleep staging automation base on the theory of evidence. IEEE Transactions on Biomedical Engineering, 36(5):503–509, 1987.
Shannahoff-Khalsa David S., Gillin J. Christian, and etc. Ultradia n rhythms of alternating cerebral hemispheric eeg dominance are coupled to rapid eye movement and non-rapid eye movement stage 4 sleep in humans. Sleep Medicine, 2(4):333–346, 2001.
N. Schaltenbrand, R. Lengelle, and J. P. Macher. Neural network model: Application to automatic analysis of human sleep. Computer and Biomedical Research, 26(2):157–171, 1993.
J. R. Smith, I. Karacan, and M. C. K. Yang. Automated analysis of the human sleep EEG. Waking and Sleeping, 2:229–237, 1978.
E. Stanus, B. Lacroix, and M. Kerkhofs et al. Automated sleep coring: A comparative reliability study of two algorithms. Electroencephalography and Clinical Neurophysiology, 66(4):448–454, 1987.
E. C. Titchmarsh. Introduction to the Theory of Fourier Integrals. Oxford University Press, 1948.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Birkhäuser Verlag Basel/Switzerland
About this paper
Cite this paper
Yang, Z., Yang, L., Qi, D. (2006). Detection of Spindles in Sleep EEGs Using a Novel Algorithm Based on the Hilbert-Huang Transform. In: Qian, T., Vai, M.I., Xu, Y. (eds) Wavelet Analysis and Applications. Applied and Numerical Harmonic Analysis. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-7778-6_40
Download citation
DOI: https://doi.org/10.1007/978-3-7643-7778-6_40
Publisher Name: Birkhäuser Basel
Print ISBN: 978-3-7643-7777-9
Online ISBN: 978-3-7643-7778-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)