Methods Inf Med - Classification of sleep stages using multi-wavelet time frequency entropy and LDA.

Tópicos

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Resumo

CKGROUND: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomnographic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it.OBJECTIVES: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal.METHODS: The use of different mother wavelets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method.RESULTS: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78.CONCLUSIONS: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

Resumo Limpo

ckground process automat sleep stage score consist two major part featur extract classif featur normal extract polysomnograph record main electroencephalograph eeg signal eeg consid nonstationari signal increas complex detect differ wave itobject work present new techniqu automat sleep stage score base employ continu wavelet transform cwt linear discrimin analysi lda use differ mother wavelet detect differ wave embed eeg signalmethod use differ mother wavelet increas abil detect wave eeg signal extract featur form base cwt time frequenc entropi use three mother wavelet classif perform use linear discrimin analysi thirtytwo data set mitbih databas use evalu perform propos methodresult featur singl eeg signal extract success base time frequenc entropi use continu wavelet transform three mother wavelet propos method shown outperform classif base cwt use singl mother wavelet accuraci found kappa coeffici conclus work shown wavelet time frequenc entropi provid power tool featur extract nonstationari eeg signal accuraci classif procedur improv use multipl wavelet compar use singl wavelet time frequenc entropi

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