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.
Chapter PDF
Similar content being viewed by others
References
De Gennaro, L., Ferrara, M.: Sleep spindles: an overview. Sleep Med. Rev. 7, 423–440 (2003)
da Costa, J.C., Ortigueira, M.D., Batista, A.: ARMA Modelling of Sleep Spindles. In: Camarinha-Matos, L.M. (ed.) DoCEIS 2011. IFIP AICT, vol. 349, pp. 341–348. Springer, Heidelberg (2011)
Steriade, M., Jones, E.G., Llinas: Thalamic Oscillations and Signaling. Neuroscience Institute Publications, John Wiley & Sons, New York (1990)
Rechtschaffen, A., Kales, A.: A manual of standardised terminology, techniques and scoring system for sleep stages of human subjects. Public Health Service, U.S. Government Printing Office, Washington, DC (1968)
Kizilkaya, A., Kayran, A.H.: ARMA model parameter estimation based on the equivalent MA approach. Digital Signal Processing 16(6) (2006)
Costa, J.C., Ortigueira, M.D., Batista, A., Paiva, T.: An Automatic Sleep Spindle detector based on WT, STFT and WMSD. International Journal of the World Academy of Science, Engineering and Technology 68, 1298–1301 (2012)
Proakis, J., Manolakis, D.: Digital Signal Processing, 4th edn. Prentice-Hall (2006)
Omerhodzic, I., Avdakovic, S., Nuhanovic, A., Dizdarevic, K., Rotim, K.: Energy Distribution of EEG Signal Components by Wavelet Transform, pp. 45–60. InTech Publishing (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 IFIP International Federation for Information Processing
About this paper
Cite this paper
da Costa, J.C., Ortigueira, M.D., Batista, A., Paiva, T. (2013). ARMA Modelling for Sleep Disorders Diagnose. In: Camarinha-Matos, L.M., Tomic, S., Graça, P. (eds) Technological Innovation for the Internet of Things. DoCEIS 2013. IFIP Advances in Information and Communication Technology, vol 394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37291-9_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-37291-9_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37290-2
Online ISBN: 978-3-642-37291-9
eBook Packages: Computer ScienceComputer Science (R0)