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Sleep spindles detection from human sleep EEG signals using autoregressive (AR) model: a surrogate data approach
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作者 Venkatakrishnan Perumalsamy sangeetha sankaranarayanan Sukanesh Rajamony 《Journal of Biomedical Science and Engineering》 2009年第5期294-303,共10页
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 p... 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. 展开更多
关键词 AR MODEL LPC SLEEP SPINDLES Surrogate Data
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