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ARMA Modelling for Sleep Disorders Diagnose

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.
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João Costa, Manuel Ortigueira, Arnaldo Batista, Teresa Paiva. ARMA Modelling for Sleep Disorders Diagnose. 4th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Apr 2013, Costa de Caparica, Portugal. pp.271-278, ⟨10.1007/978-3-642-37291-9_29⟩. ⟨hal-01348764⟩

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