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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.

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• TITLE: Sleep spindles detection from human sleep EEG signals using autoregressive (AR) model: a surrogate data approach

  AUTHORS: Venkatakrishnan Perumalsamy, Sangeetha Sankaranarayanan, Sukanesh Rajamony

  KEYWORDS: AR Model; LPC; Sleep Spindles; Surrogate Data

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.2 No.5, September 17, 2009

  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.