Int J Neural Syst - Combination of heterogeneous EEG feature extraction methods and stacked sequential learning for sleep stage classification.

Tópicos

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Resumo

This work proposes a methodology for sleep stage classification based on two main approaches: the combination of features extracted from electroencephalogram (EEG) signal by different extraction methods, and the use of stacked sequential learning to incorporate predicted information from nearby sleep stages in the final classifier. The feature extraction methods used in this work include three representative ways of extracting information from EEG signals: Hjorth features, wavelet transformation and symbolic representation. Feature selection was then used to evaluate the relevance of individual features from this set of methods. Stacked sequential learning uses a second-layer classifier to improve the classification by using previous and posterior first-layer predicted stages as additional features providing information to the model. Results show that both approaches enhance the sleep stage classification accuracy rate, thus leading to a closer approximation to the experts' opinion.

Resumo Limpo

work propos methodolog sleep stage classif base two main approach combin featur extract electroencephalogram eeg signal differ extract method use stack sequenti learn incorpor predict inform nearbi sleep stage final classifi featur extract method use work includ three repres way extract inform eeg signal hjorth featur wavelet transform symbol represent featur select use evalu relev individu featur set method stack sequenti learn use secondlay classifi improv classif use previous posterior firstlay predict stage addit featur provid inform model result show approach enhanc sleep stage classif accuraci rate thus lead closer approxim expert opinion

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