Sleep spindles detection from human sleep EEG signals using autoregressive (AR) model: a surrogate data approach
- 1 Department of Information Technology, Thiagarajar College of Engineering, Madurai, India
- 2 Department of Electrical and Electronics Engineering, Sethu Instituite of Technology, Madurai, India
- 3 Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai, India
Abstract
A new algorithm for the detection of sleep spindles from human sleep EEG with surrogate data approach is presented. Surrogate data ap-proach is the state of the art technique for nonlinear spectral analysis. In this paper, by developing autoregressive (AR) models on short segment of the EEG is described as a superposition of harmonic oscillating with damping and frequency in time. Sleep spindle events are detected, whenever the damping of one or more frequencies falls below a prede-fined threshold. Based on a surrogate data, a method was proposed to test the hypothesis that the original data were generated by a linear Gaussian process. This method was tested on human sleep EEG signal. The algorithm work well for the detection of sleep spindles and in addition the analysis reveals the alpha and beta band activities in EEG. The rigorous statistical framework proves essential in establishing these results.
- Buzsaki, G., (2006) Rhythms of the brain, Oxford, U. K: Ox-ford University Press.
- Chen, Y. H., Bressler, S. L., Knuth, K. H., Truccolo, W. A., and Ding, M. Z., (2006) Stochastic modeling of neurobiologi-cal time series: Power, coherence, granger causality, and sepa-ration of evoked response from ongoing activity, Chaos, 16.
- Barlow, J. S., (1979) Computerized clinical electroencephalo-graphy in perspective, IEEE Transactions Biomed. Eng., 26 (7), 377–391..
- Franaszczuk, P. J. and Blinowska, K. J., (1985) Linear model of brain electric activity EEG as a superposition of damped oscillatory models, Biol. Cybern., 53, 19–25.
- Olbrich, E. and Achermann, P., (2003) Oscillatory events in the human sleep EEG detection and properties, Elsevier Sci-ence March., 1–6.
- Olbrich, E., Achermann, P., and Meier, P. F., (2002) Dynamics of human sleep EEG, Neuro computing CNS.
- Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., and Farmer, J. D., (1992) Testing for linearity in time series: The method of surrogate data, Phys. D., 58, 77–94,.
- Wang, X., Chen, Y. H., and Ding, M. Z., (2007) Testing for statistical significance in Bispectra: A surrogate data approach and application to neuroscience, IEEE Transactions on Bio-medical Engineering, 54(11), 1974–1982.
- Wu, W. B., (2005) Fourier transform of stationary processes, Proc. Amer. Math. Soc., 133, 285–293.
- Bendat, J. and Persol, A., (1986) Random data analysis and measurement procedures, 2nd ed. New York: Wiley.
- Marsagli, G. and Tsang, W. W., (2000) The ziggurat method for generating random variables, J. Stat. Softw., 5, 1–6.
- Timmer J., “On Surrogate data testing for linearity based on the periodogram”, eprints arxiv: comp-gas/9509003, 1995.
- Evans, M., Hastings, N., and Peacock, B., (1993) Statistical distributions, 2nd ed, New York: Wiley.
- Schwilden, H. and Jeleazcov, C., (2002) Does the EEG during isoflurane/alfentanil anesthesia differ from linear random data, J. Clin. Monit, Comput., 17, 449–457.
- Schack, B., Vath, N., Petsche, H., Geissler, H. G., and Moller E., (2002) Phase coupling of theta-gamma EEG rhythms dur-ing short term memory processing, Int. J. Psychophys., 44, 143–163,.
- Shils, J. L., Litt, M., Skolnick, B. E., and Stecker, M. M., (1996) Bispectral analysis of visual interactions in humans, Electroencephalography Clinical Neurophys., l98, 113–125.