J Med Syst - Identification of deep sleep and awake with computational EEG measures.

Tópicos

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{ signal(2180) analysi(812) frequenc(800) }
{ extract(1171) text(1153) clinic(932) }
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Resumo

The objective of the present work was to examine identification of deep sleep and awake with computational analysis of sleep EEG traces from central brain regions. All-night EEG traces from a total of 56 male subjects, 22 healthy control subjects and 34 age-matched apnea patients, were examined. A spectral mean frequency measure, a Hilbert transform based EEG amplitude and a correlation coefficient method were compared. The EEG amplitude provided a good identification of deep sleep, reaching 86.25% but was relatively poor in the identification of wakefulness, reaching 39.06%. Mean frequency provided a relatively good identification of deep sleep and awake, reaching 84.66% and 77.67%, respectively, while the correlation coefficient produced the lowest results of 37.89% and 44.43%. Optimal threshold values for deep sleep and awake identification were determined as 4.20 and 9.76 Hz, respectively, for the mean frequency measure. Mean frequency measure can be used to provide overall context information about sleep depth for automated sleep EEG analysis methods.

Resumo Limpo

object present work examin identif deep sleep awak comput analysi sleep eeg trace central brain region allnight eeg trace total male subject healthi control subject agematch apnea patient examin spectral mean frequenc measur hilbert transform base eeg amplitud correl coeffici method compar eeg amplitud provid good identif deep sleep reach relat poor identif wake reach mean frequenc provid relat good identif deep sleep awak reach respect correl coeffici produc lowest result optim threshold valu deep sleep awak identif determin hz respect mean frequenc measur mean frequenc measur can use provid overal context inform sleep depth autom sleep eeg analysi method

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