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Sleep fragmentation and respiratory disturbance measures are used in the assessment of obstructive sleep apnea (OSA) but have proved to be disappointingly poor correlates of daytime sleepiness. This study investigates the ability of electroencephalograph (EEG) and non-EEG sleep fragmentation indices to predict both presenting sleepiness and the improvement in sleepiness with subsequent nasal continuous positive airway pressure (nCPAP) therapy (nCPAP responsive sleepiness). Forty-one patients (36 men, 5 women), ranging from nonsnorers to severe OSA (> 4% O2 dip rate, median 11.1, range 0.4 to 76.5), had polysomnography with microarousal scoring, computerized EEG analysis, autonomic arousal detection, and body movement analysis. All patients received a trial of nCPAP regardless of sleep study outcome. Spearman's correlation analysis showed significant and similar associations between all sleep fragmentation indices with both pretreatment and nCPAP responsive sleepiness. There was no deterioration in sleepiness on nCPAP in the nonsnorers. Using stepwise multiple regression analysis, the best predictor of nCPAP responsive subjective and objective sleepiness was body movement index, explaining 38% and 43% of the variance, respectively. Variability in EEG sleep depth, quantified from computerized EEG analysis, was the only other index to contribute to these models. Together these indices explained 44% and 51% of the subjective and objective response to nCPAP, respectively. These results suggest that sleep fragmentation indices are useful for identifying OSA patients with sleepiness likely to respond to nCPAP.

Original publication

DOI

10.1164/ajrccm.158.3.9711033

Type

Journal article

Journal

Am J Respir Crit Care Med

Publication Date

09/1998

Volume

158

Pages

778 - 786

Keywords

Adult, Arousal, Circadian Rhythm, Electroencephalography, Female, Follow-Up Studies, Forecasting, Heart Rate, Humans, Male, Middle Aged, Movement, Neural Networks (Computer), Oxygen, Polysomnography, Positive-Pressure Respiration, Regression Analysis, Signal Processing, Computer-Assisted, Sleep, Sleep Apnea Syndromes, Sleep Stages, Snoring