Sleep EEG signal analysis based on correlation graph similarity coupled with an ensemble extreme machine learning algorithm

Background: Sleep plays an essential role in repairing and healing human mental and physical health. Developing an efficient method for scoring electroencephalogram (EEG) sleep stages is expected to help medical specialists in the early diagnosis of sleep disorders. Method: In this paper, a novel technique is proposed for classifying sleep stages EEG signals using correlation graphs. First, each 30 s EEG segment is divided into a set of sub-segments. The dimensionality of each sub-segment is reduced by using a statistical model. Second, each EEG segment is transferred into a graph considering each sub-segment as a node in a graph, and a link between each pair of nodes is calculated based on their correlation coefficient. Graph's modularity is used as input features into an ensemble classifier. Results: Different community detection algorithm based correlation graph are investigated to discern the most effective features to reveal the differences between EEG sleep stages. A combination of various classification techniques: a least square vector machine (LS-SVM), k-means, Naive Bayes, Fuzzy C-means, k-nearest, and logistic regression are tested using multi criteria decision making (MCDM) to design an ensemble classifier. Based on the results of the MCDM, the best four: LS-SVM, Naive Bayes, logistic regression and k-nearest are integrated, to finally utilise as an ensemble classifier to categorise the graph's characteristics. The results obtained from the ensemble classifier are compared with those from the individual classifiers. The performance of the proposed method is compared with state of the art of sleep stages classification. The experimental results showed that the EEG sleep classification based on correlation graphs are able to achieve better recognition results than the existing state of the art techniques. (C) 2019 Elsevier Ltd. All rights reserved.