Comput. Biol. Med. - An ensemble system for automatic sleep stage classification using single channel EEG signal.

Tópicos

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Resumo

The present work aims at automatic identification of various sleep stages like, sleep stages 1, 2, slow wave sleep (sleep stages 3 and 4), REM sleep and wakefulness from single channel EEG signal. Automatic scoring of sleep stages was performed with the help of pattern recognition technique which involves feature extraction, selection and finally classification. Total 39 numbers of features from time domain, frequency domain and from non-linear analysis were extracted. After extraction of features, SVM based recursive feature elimination (RFE) technique was used to find the optimum number of feature subset which can provide significant classification performance with reduced number of features for the five different sleep stages. Finally for classification, binary SVMs were combined with one-against-all (OAA) strategy. Careful extraction and selection of optimum feature subset helped to reduce the classification error to 8.9% for training dataset, validated by k-fold cross-validation (CV) technique and 10.61% in the case of independent testing dataset. Agreement of the estimated sleep stages with those obtained by expert scoring for all sleep stages of training dataset was 0.877 and for independent testing dataset it was 0.8572. The proposed ensemble SVM-based method could be used as an efficient and cost-effective method for sleep staging with the advantage of reducing stress and burden imposed on subjects.

Resumo Limpo

present work aim automat identif various sleep stage like sleep stage slow wave sleep sleep stage rem sleep wake singl channel eeg signal automat score sleep stage perform help pattern recognit techniqu involv featur extract select final classif total number featur time domain frequenc domain nonlinear analysi extract extract featur svm base recurs featur elimin rfe techniqu use find optimum number featur subset can provid signific classif perform reduc number featur five differ sleep stage final classif binari svms combin oneagainstal oaa strategi care extract select optimum featur subset help reduc classif error train dataset valid kfold crossvalid cv techniqu case independ test dataset agreement estim sleep stage obtain expert score sleep stage train dataset independ test dataset propos ensembl svmbase method use effici costeffect method sleep stage advantag reduc stress burden impos subject

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