Artif Intell Med - Automatic sleep scoring: a search for an optimal combination of measures.

Tópicos

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Resumo

JECTIVE: The objective of this study is to find the best set of characteristics of polysomnographic signals for the automatic classification of sleep stages.METHODS: A selection was made from 74 measures, including linear spectral measures, interdependency measures, and nonlinear measures of complexity that were computed for the all-night polysomnographic recordings of 20 healthy subjects. The adopted multidimensional analysis involved quadratic discriminant analysis, forward selection procedure, and selection by the best subset procedure. Two situations were considered: the use of four polysomnographic signals (EEG, EMG, EOG, and ECG) and the use of the EEG alone.RESULTS: For the given database, the best automatic sleep classifier achieved approximately an 81% agreement with the hypnograms of experts. The classifier was based on the next 14 features of polysomnographic signals: the ratio of powers in the beta and delta frequency range (EEG, channel C3), the fractal exponent (EMG), the variance (EOG), the absolute power in the sigma 1 band (EEG, C3), the relative power in the delta 2 band (EEG, O2), theta/gamma (EEG, C3), theta/alpha (EEG, O1), sigma/gamma (EEG, C4), the coherence in the delta 1 band (EEG, O1-O2), the entropy (EMG), the absolute theta 2 (EEG, Fp1), theta/alpha (EEG, Fp1), the sigma 2 coherence (EEG, O1-C3), and the zero-crossing rate (ECG); however, even with only four features, we could perform sleep scoring with a 74% accuracy, which is comparable to the inter-rater agreement between two independent specialists.CONCLUSIONS: We have shown that 4-14 carefully selected polysomnographic features were sufficient for successful sleep scoring. The efficiency of the corresponding automatic classifiers was verified and conclusively demonstrated on all-night recordings from healthy adults.

Resumo Limpo

jectiv object studi find best set characterist polysomnograph signal automat classif sleep stagesmethod select made measur includ linear spectral measur interdepend measur nonlinear measur complex comput allnight polysomnograph record healthi subject adopt multidimension analysi involv quadrat discrimin analysi forward select procedur select best subset procedur two situat consid use four polysomnograph signal eeg emg eog ecg use eeg aloneresult given databas best automat sleep classifi achiev approxim agreement hypnogram expert classifi base next featur polysomnograph signal ratio power beta delta frequenc rang eeg channel c fractal expon emg varianc eog absolut power sigma band eeg c relat power delta band eeg o thetagamma eeg c thetaalpha eeg o sigmagamma eeg c coher delta band eeg oo entropi emg absolut theta eeg fp thetaalpha eeg fp sigma coher eeg oc zerocross rate ecg howev even four featur perform sleep score accuraci compar interrat agreement two independ specialistsconclus shown care select polysomnograph featur suffici success sleep score effici correspond automat classifi verifi conclus demonstr allnight record healthi adult

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