ENSEMBLE SVM METHOD FOR AUTOMATIC SLEEP STAGE CLASSIFICATION

Abstract

Sleep scoring is used as a diagnostic technique in the diagnosis and treatment of sleep disorders. Automated sleep scoring is crucial, since the large volume of data should be analyzed visually by the sleep specialists which is burdensome, time-consuming tedious, subjective, and error prone. Therefore, automated sleep stage classification is a crucial step in sleep research and sleep disorder diagnosis. In this paper, a robust system, consisting of three modules, is proposed for automated classification of sleep stages from the single-channel electroencephalogram (EEG). In the first module, signals taken from Pz-Oz electrode were denoised using multiscale principal component analysis. In the second module, the most informative features are extracted using discrete wavelet transform (DWT), and then, statistical values of DWT subbands are calculated. In the third module, extracted features were fed into an ensemble classifier, which can be called as rotational support vector machine (RotSVM). The proposed classifier combines advantages of the principal component analysis and SVM to improve classification performances of the traditional SVM. The sensitivity and accuracy values across all subjects were 84.46% and 91.1%, respectively, for the five-stage sleep classification with Cohen’s kappa coefficient of 0.88. Obtained classification performance results indicate that, it is possible to have an efficient sleep monitoring system with a single-channel EEG, and can be used effectively in medical and home-care applications.

Existing System

Principal component analysis (PCA), independent component analysis, denoising source separation,

Proposed System

The basic structure of the system proposed in this paper is given. 1. It can be seen that it has three main modules: 1) signal denoising; 2) feature extraction/dimension reduction; and 3) classification. In this section, we provide detailed explanation of each module. In this paper, we propose a modified SVM which is called RotSVM. The contribution of this paper is to use MSPCA for denoising and rotational SVM (RotSVM) for classification to create a reliable and efficient automated system for sleep stage identification and classification where the features will be extracted from a single-channel EEG signals. After segmenting Pz-Oz EEG channel signals, MSPCA is used to denoise the EEG signals in the preprocessing module. After denoising the EEG signals, in the second module, informative features from the denoised signals are extracted using discreteWT (DWT), since it can efficiently decompose an EEG signal into different frequency bands relevant to this paper: delta (0.5–3 Hz), theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz), and low gamma (30–50 Hz). Furthermore, in order to reduce the dimension of data, statistical values of DWT subbands are calculated to represent the distribution of wavelet coefficients in a better way. The extracted features are fed into the classifier in the third module.

Architecture

BLOCK DIAGRAM