Accurate method for sleep stages classification using discriminated features and single EEG channel

[Display omitted] Sleep classification can be time-consuming and challenging for professionals since electroencephalograms (EEGs) need to be segmented, evaluated, and manually annotated. This study aims to investigate the possibility of automating the classification of sleep stages to speed up the identification of sleep problems and assist medical professionals. In this study, Wavelet Transform (WT) and Residue Decomposition (RD) are proposed for feature extraction. First, the input signals are subjected to the Wavelet Transform of depth five with Daubechies order four (WTDB4) every 30 s at a frequency of 100 Hz. The Standard Deviation (SD), Sample Entropy (SE), and Zero Crossing Rate (ZCR) are then extracted from selected wavelet components. Following that, for each sub-band, the mean residue value and Median Absolute Deviation (MAD) of this value are determined. In addition, the Least Square Support Vector Machine (LSSVM) classifier is used to classify EEG signals obtained from the Sleep EDF single-channel database (Fpz-Cz). Also, the superiority of the LS-SVM performance over other classifiers such as Decision Tree (DT), Ensemble (Ens), Bagging (Bag), Random Subspace (RS), Random Forest (RF), Stacking (St), and Artificial Neural Networks (ANN) is demonstrated. Additionally, since single-channel EEGs are only used to categorize, the hardware implementation costs are low. Compared to other methods, numerical computer simulations show good classification results with the proposed method.