Share Email Print
cover

Proceedings Paper

Nonlinear signal processing of electroencephalograms for automated sleep monitoring
Author(s): D. Wilson; D. D. Rowlands; Daniel A. James; T. Cutmore
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

An automated classification technique is desirable to identify the different stages of sleep. In this paper a technique for differentiating the characteristics of each sleep phase has been developed. This is an ideal pre-processor stage for classifying systems such as neural networks. A wavelet based continuous Morlet transform was developed to analyse the EEG signal in both the time and frequency domain. Test results using two 100 epoch EEG test data sets from pre-recorded EEG data are presented. Key rhythms in the EEG signal were identified and classified using the continuous wavelet transform. The wavelet results indicated each sleep phase contained different rhythms and artefacts (noise from muscle movement in the EEG); providing proof that an EEG can be classified accordingly. The coefficients founded by the wavelet transform have been emphasised by statistical techniques. Hypothesis testing was used to highlight major differences between adjacent sleep stages. Various signal processing methods such as power spectrum density and the discrete wavelet transform have been used to emphasise particular characteristics in an EEG. By implementing signal processing methods on an EEG data set specific rules for each sleep stage have been developed suitable for a neural network classification solution.

Paper Details

Date Published: 16 February 2005
PDF: 11 pages
Proc. SPIE 5651, Biomedical Applications of Micro- and Nanoengineering II, (16 February 2005); doi: 10.1117/12.582317
Show Author Affiliations
D. Wilson, Griffith Univ. (Australia)
D. D. Rowlands, Griffith Univ. (Australia)
Daniel A. James, Griffith Univ. (Australia)
T. Cutmore, Griffith Univ. (Australia)


Published in SPIE Proceedings Vol. 5651:
Biomedical Applications of Micro- and Nanoengineering II
Dan V. Nicolau, Editor(s)

© SPIE. Terms of Use
Back to Top