Comput. Biol. Med. - Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal.

Tópicos

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Resumo

This study presents a novel approach for the electroencephalogram (EEG) signal quantification in which the empirical mode decomposition method, a time-frequency method designated for nonlinear and non-stationary signals, decomposes the EEG signal into intrinsic mode functions (IMF) with corresponding frequency ranges that characterize the appropriate oscillatory modes embedded in the brain neural activity acquired using EEG. To calculate the instantaneous frequency of IMFs, an algorithm was developed using the Generalized Zero Crossing method. From the resulting frequencies, two different novel features were generated: the median instantaneous frequencies and the number of instantaneous frequency changes during a 30s segment for seven IMFs. The sleep stage classification for the daytime sleep of 20 healthy babies was determined using the Support Vector Machine classification algorithm. The results were evaluated using the cross-validation method to achieve an approximately 90% accuracy and with new examinee data to achieve 80% average accuracy of classification. The obtained results were higher than the human experts' agreement and were statistically significant, which positioned the method, based on the proposed features, as an efficient procedure for automatic sleep stage classification. The uniqueness of this study arises from newly proposed features of the time-frequency domain, which bind characteristics of the sleep signals to the oscillation modes of brain activity, reflecting the physical characteristics of sleep, and thus have the potential to highlight the congruency of twin pairs with potential implications for the genetic determination of sleep.

Resumo Limpo

studi present novel approach electroencephalogram eeg signal quantif empir mode decomposit method timefrequ method design nonlinear nonstationari signal decompos eeg signal intrins mode function imf correspond frequenc rang character appropri oscillatori mode embed brain neural activ acquir use eeg calcul instantan frequenc imf algorithm develop use general zero cross method result frequenc two differ novel featur generat median instantan frequenc number instantan frequenc chang s segment seven imf sleep stage classif daytim sleep healthi babi determin use support vector machin classif algorithm result evalu use crossvalid method achiev approxim accuraci new examine data achiev averag accuraci classif obtain result higher human expert agreement statist signific posit method base propos featur effici procedur automat sleep stage classif uniqu studi aris newli propos featur timefrequ domain bind characterist sleep signal oscil mode brain activ reflect physic characterist sleep thus potenti highlight congruenc twin pair potenti implic genet determin sleep

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