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Published in:
2013 | OriginalPaper | Chapter
ARMA Modelling for Sleep Disorders Diagnose
Authors : João Caldas da Costa, Manuel Duarte Ortigueira, Arnaldo Batista, Teresa Paiva
Published in: Technological Innovation for the Internet of Things
Publisher: Springer Berlin Heidelberg
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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.