Assessment of Time, Frequency, and Wavelet Packet Transform Features Extracted from EEG for Sleep Staging using Self Organizing Maps (SOM)

Ebrahimi, Faride; Mikaili, Mohammad

doi:10.22041/ijbme.2010.13331

Document Type : Full Research Paper

Authors

Faride Ebrahimi ¹
Mohammad Mikaili ²

¹ PhD Candidate, Bioelectric Group, School of Electrical Engineering, University of Tehran

² Associate Professor, Biomedical Engineering Group, School of Engineering, Shahed University

10.22041/ijbme.2010.13331

Abstract

Different biological signals including EEG, EOG, and EMG are recorded in sleep labs to diagnose sleep disorders. Data recorded during sleep is usually analyzed by sleep specialists visually. Since the sleep data is usually recorded for a long time period- namely a whole night- its visual inspection and classification is a very demanding and time consuming task so automatic analysis can definitely facilitate that. The key to automatic sleep staging is to extract suitable features. In the current study two classes of features are extracted from EEG signal. The first group is the features calculated from the coefficients of wavelet packet transformation (WPT) and the second group consists of a number of frequency features and a time feature, the amplitude of EEG signal itself. These two sets of features were separately mapped on a two dimensional space by SOM neural networks. The mappings indicated that these features are highly discriminative in separating sleep stages automatically. The data extracted from awake and deep sleep EEGs were mapped on two totally different regions. The mapping also indicated that EEG signal is not enough to separate stages thoroughly, as extracted data from EEG during REM and the first stage of NREM are mapped on the same region. Data extracted from EEG signals in the second stage overlapped with other stages which are in agreement with physiological definition of sleep stages.

Keywords

Main Subjects

Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing

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Iranian Journal of Biomedical Engineering

Assessment of Time, Frequency, and Wavelet Packet Transform Features Extracted from EEG for Sleep Staging using Self Organizing Maps (SOM)

References

References

Volume 4, Issue 2 - Serial Number 2
June 2010
Pages 97-108

Assessment of Time, Frequency, and Wavelet Packet Transform Features Extracted from EEG for Sleep Staging using Self Organizing Maps (SOM)

References

References

Volume 4, Issue 2 - Serial Number 2June 2010Pages 97-108

Volume 4, Issue 2 - Serial Number 2
June 2010
Pages 97-108