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
