Methods Inf Med - Investigation of an automatic sleep stage classification by means of multiscorer hypnogram.

Tópicos

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Resumo

JECTIVES: Scoring sleep visually based on polysomnography is an important but time-consuming element of sleep medicine. Whereas computer software assists human experts in the assignment of sleep stages to polysomnogram epochs, their performance is usually insufficient. This study evaluates the possibility to fully automatize sleep staging considering the reliability of the sleep stages available from human expert sleep scorers.METHODS: We obtain features from EEG, ECG and respiratory signals of polysomnograms from ten healthy subjects. Using the sleep stages provided by three human experts, we evaluate the performance of linear discriminant analysis on the entire polysomnogram and only on epochs where the three experts agree in their sleep stage scoring.RESULTS: We show that in polysomnogram intervals, to which all three scorers assign the same sleep stage, our algorithm achieves 90% accuracy. This high rate of agreement with the human experts is accomplished with only a small set of three frequency features from the EEG. We increase the performance to 93% by including ECG and respiration features. In contrast, on intervals of ambiguous sleep stage, the sleep stage classification obtained from our algorithm, agrees with the human consensus scorer in approximately 61%.CONCLUSIONS: These findings suggest that machine classification is highly consistent with human sleep staging and that error in the algorithm's assignments is rather a problem of lack of well-defined criteria for human experts to judge certain polysomnogram epochs than an insufficiency of computational procedures.

Resumo Limpo

jectiv score sleep visual base polysomnographi import timeconsum element sleep medicin wherea comput softwar assist human expert assign sleep stage polysomnogram epoch perform usual insuffici studi evalu possibl fulli automat sleep stage consid reliabl sleep stage avail human expert sleep scorersmethod obtain featur eeg ecg respiratori signal polysomnogram ten healthi subject use sleep stage provid three human expert evalu perform linear discrimin analysi entir polysomnogram epoch three expert agre sleep stage scoringresult show polysomnogram interv three scorer assign sleep stage algorithm achiev accuraci high rate agreement human expert accomplish small set three frequenc featur eeg increas perform includ ecg respir featur contrast interv ambigu sleep stage sleep stage classif obtain algorithm agre human consensus scorer approxim conclus find suggest machin classif high consist human sleep stage error algorithm assign rather problem lack welldefin criteria human expert judg certain polysomnogram epoch insuffici comput procedur

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