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Methods Inf Med 2010; 49(03): 230-237
DOI: 10.3414/ME09-01-0054
Original Articles
Schattauer GmbH

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

L. Fraiwan
1   Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
,
K. Lweesy
1   Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
,
N. Khasawneh
2   Computer Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
,
M. Fraiwan
2   Computer Engineering Department, Jordan University of Science and Technology, Irbid, Jordan
,
H. Wenz
3   Thoracic Clinic, University of Heidelberg, Heidelberg, Germany
,
H. Dickhaus
4   Medical Informatics Department, University of Heidelberg, Heidelberg, Germany
› Author Affiliations
Further Information

Publication History

received: 19 June 2009

accepted: 04 January 2009

Publication Date:
17 January 2018 (online)

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Summary

Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it.

Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal.

Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method.

Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78.

Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

Keywords

Sleep stage scoring - multi-wavelets - time frequency entropy - linear discriminant analysis
 

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