ARMA modelling for sleep disorders diagnose

João Caldas Da Costa, Manuel Duarte Ortigueira, Arnaldo Batista, Teresa Paiva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationTechnological Innovation for the Internet of Things. DoCEIS 2013
EditorsL. M. Camarinha-Matos, S. Tomic, P. Graça
Place of PublicationBerlin, Heidelberg
PublisherSpringer
Pages271-278
Number of pages8
ISBN (Electronic)978-3-642-37291-9
ISBN (Print)978-3-642-37290-2
DOIs
Publication statusPublished - 2013
Event4th IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2013 - Costa de Caparica, Portugal
Duration: 15 Apr 201317 Apr 2013

Publication series

NameIFIP Advances in Information and Communication Technology
PublisherSpringer
Volume394
ISSN (Print)1868-4238

Conference

Conference4th IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2013
CountryPortugal
CityCosta de Caparica
Period15/04/1317/04/13

Keywords

  • ARMA
  • EEG
  • Parasomnia REM
  • Sleep Spindles

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