Differences in EEG sleep spindles constitute a promising indicator of neurodegenerative disorders. In this paper an ARMA modelling to sleep spindles is proposed and tested. The primary objective is to distinguish, via poles and zeros location, between regular, elderly and dementia subjects. In order to achieve this goal, a model validation has been done.

Abstract. Sleep spindles are the most interesting hallmark of stage 2 sleep EEG. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload. In this paper two different approaches are used for the automatic detection of sleep spindles: Short Time Fourier Transform and Automatic Visual Scoring. The results obtained using both methods are compared with human expert scorers.

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Abstract. Differences in EEG sleep spindles constitute a promising indicator of sleep disorders. In this paper Sleep Spindles are extracted from real EEG data using a triple (Short Time Fourier Transform-STFT; Wavelet Transform-WT; Wave Morphology for Spindle Detection-WMSD) algorithm. After the detection, an Autoregressive-moving-average (ARMA) model is applied to each Spindle and finally the ARMA's coefficients' mean is computed in order to find a model for each patient. Regarding only the position of real poles and zeros, it is possible to distinguish normal from Parasomnia REM subjects.