J Med Syst - A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms.

Tópicos

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Resumo

Sleep scoring is one of the most important diagnostic methods in psychiatry and neurology. Sleep staging is a time consuming and difficult task undertaken by sleep experts. This study aims to identify a method which would classify sleep stages automatically and with a high degree of accuracy and, in this manner, will assist sleep experts. This study consists of three stages: feature extraction, feature selection from EEG signals, and classification of these signals. In the feature extraction stage, it is used 20 attribute algorithms in four categories. 41 feature parameters were obtained from these algorithms. Feature selection is important in the elimination of irrelevant and redundant features and in this manner prediction accuracy is improved and computational overhead in classification is reduced. Effective feature selection algorithms such as minimum redundancy maximum relevance (mRMR); fast correlation based feature selection (FCBF); ReliefF; t-test; and Fisher score algorithms are preferred at the feature selection stage in selecting a set of features which best represent EEG signals. The features obtained are used as input parameters for the classification algorithms. At the classification stage, five different classification algorithms (random forest (RF); feed-forward neural network (FFNN); decision tree (DT); support vector machine (SVM); and radial basis function neural network (RBF)) classify the problem. The results, obtained from different classification algorithms, are provided so that a comparison can be made between computation times and accuracy rates. Finally, it is obtained 97.03 % classification accuracy using the proposed method. The results show that the proposed method indicate the ability to design a new intelligent assistance sleep scoring system.

Resumo Limpo

sleep score one import diagnost method psychiatri neurolog sleep stage time consum difficult task undertaken sleep expert studi aim identifi method classifi sleep stage automat high degre accuraci manner will assist sleep expert studi consist three stage featur extract featur select eeg signal classif signal featur extract stage use attribut algorithm four categori featur paramet obtain algorithm featur select import elimin irrelev redund featur manner predict accuraci improv comput overhead classif reduc effect featur select algorithm minimum redund maximum relev mrmr fast correl base featur select fcbf relieff ttest fisher score algorithm prefer featur select stage select set featur best repres eeg signal featur obtain use input paramet classif algorithm classif stage five differ classif algorithm random forest rf feedforward neural network ffnn decis tree dt support vector machin svm radial basi function neural network rbf classifi problem result obtain differ classif algorithm provid comparison can made comput time accuraci rate final obtain classif accuraci use propos method result show propos method indic abil design new intellig assist sleep score system

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