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.
Highlights
Oscillatory signal activities are ubiquitous in the biomedical signals [1]
We start with AR model of order p and proceed to outline the procedure for generating surrogate data
In this study sleep spindles were detected in 1s overlapped segments, wide 0.5-16Hz band containing delta and theta was used for better eye movement separation
Summary
Oscillatory signal activities are ubiquitous in the biomedical signals [1]. Multielectrode recordings provide the opportunity to study signal oscillations from a network perspective. To assess signal interactions in the frequency domain, one often applies methods, such as ordinary coherence and Granger causality spectra [2] that are formulated within the frame work of linear stochastic process. Electroencephalogram (EEG) is one of the most important electrophysiological techniques used in human clinical and basic sleep research. In 1979 Barlow proposed linear modeling system which has a longlasting history in EEG analysis [3]. The models are mainly considered as a mathematical description of the signal and less as a biophysical model of the underlying neuronal mechanisms
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