ARMA Modelling for Sleep Disorders Diagnose

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 (STFT, WT and WMSD) algorithm; this algorithm is reported to have sensitivity and a specificity of 94%. After the detection, an 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 languageUnknown
Title of host publicationIFIP Advances in Information and Communication Technology
Pages271-278
Volume394
DOIs
Publication statusPublished - 1 Jan 2013
Event4th IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2013 -
Duration: 1 Jan 2013 → …

Conference

Conference4th IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2013
Period1/01/13 → …

Cite this

Batista, A. M. G., & Ortigueira, M. D. (2013). ARMA Modelling for Sleep Disorders Diagnose. In IFIP Advances in Information and Communication Technology (Vol. 394, pp. 271-278) https://doi.org/10.1007/978-3-642-37291-9_29