Efficient feature selection for sleep staging based on maximal overlap discrete wavelet transform and SVM

Abstract

In this paper, a novel algorithm is proposed with application in sleep/awake detection and in multiclass sleep stage classification (awake, non rapid eye movement (NREM) sleep and REM sleep). In turn, NREM is further divided into three stages denoted here by S1, S2, and S3. Six electroencephalographic (EEG) and two electro-oculographic (EOG) channels were used in this study. The maximum overlap discrete wavelet transform (MODWT) with the multi-resolution Analysis is applied to extract relevant features from EEG and EOG signals. The extracted feature set is transformed and normalized to reduce the effect of extreme values of features. A set of significant features are selected by mRMR which is a powerful feature selection method. Finally the selected feature set is classified using support vector machines (SVMs). The system achieved 95.0% of average accuracy for sleep/awake detection. As concerns the multiclass case, the average accuracy of sleep stages classification was 93.0%.

Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels

“…Accordingly, [1,[6][7][8]. Different parametric and nonparametric methods have been applied in the classification process such as random forest classifiers, artificial neural networks (ANN), fuzzy logic, the nearest neighbour, linear discriminant analysis (LDA,) support vector machine (SVM) and kernel logistic regression (KLR) [6][7][8][9][10][11][12]. Classification accuracies vary widely among the ASSC methods reported in scientific literature.…”

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

“…In some ASSC systems, feature selection and/or dimensionality reduction is performed prior to the classification stage to reduce the number of features, or generate new low-dimensional features that are derived from the input features. Examples can be found in [9,41,51,60]. Finally, the extracted attributes are passed to one or more classifiers to categorize human sleep stages.…”

“…Some ASSC schemes have been designed using the extreme values of features, regardless of very high dimensional vectors, feature redundancy and noisy data that affect the classification process [5,51]. In this case, dimensionality reduction and/or feature selection algorithms are used to find the most discriminative features and to keep the dimensionality of features as low as possible [8].…”

Automatic brain state classification system using double channel of EEG signal from rat brain

“…Second, in the classification process, many adaptive classification techniques are used to classify the awake and sleep states such as Adaptive Neural Fuzzy Inference System (ANFIS) [10], Fuzzy Inference System (FIS) [11], Support Vector Machine (SVM) [12,13], Hidden Markov Model (HMM) [14] and Artificial Neural Networks (ANN) [15]. However, those techniques are high computation costs.…”