Classification of sleep stages using multi-wavelet time frequency entropy and LDA.
BACKGROUND
The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomnographic 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.


OBJECTIVE
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 wavelets 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.
Fraiwan
L
L
Biomedical Engineering Department, Jordan University of Science and Technology, Irbid, Jordan. fraiwan@just.edu.jo
Lweesy
K
K
Khasawneh
N
N
Fraiwan
M
M
Wenz
H
H
Dickhaus
H
H
D002965:Classification / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D012894:Sleep Stages