Method for detection of respiratory cycle-related EEG changes in sleep-disordered breathing

Study Objectives: In sleep-disordered breathing (SDB), visual or computerized analysis of electroencephalogram (EEG) signals shows that disruption of sleep architecture occurs in association with apneas and hypopneas. We developed a new signal analysis algorithm to investigate whether brief changes in cortical activity can also occur with individual respiratory cycles. Design: Retrospective. Setting: University sleep laboratory. Participants: A 6 year-old boy with SDB. Intervention: Polysomnography before and after clinically indicated adenotonsillectomy. Measurements: For the first 3 hours of nocturnal sleep, a computer algorithm divided nonapneic respiratory cycles into 4 segments and, for each, computed mean EEG powers within delta, theta, alpha, sigma, and beta frequency ranges. Differences between segment-specific EEG powers were tested by analysis of variance. Respiratory cycle-related EEG changes (RCREC) were quantified. Results: Preoperative RCREC were statistically significant in delta (P < .0001), theta (P < .001), and sigma (P < .0001) but not alpha or beta (P > .01) ranges. One year after the operation, RCREC in all ranges showed statistical significance (P < .01), but delta, theta, and sigma RCREC had decreased, whereas alpha and beta RCREC had increased. Preoperative RCREC also were demonstrated in a sequence of 101 breaths that contained no apneas or hypopneas (P < .0001). Several tested variations in the signal-analysis approach, including analysis of the entire nocturnal polysomnogram, did not meaningfully improve the significance of RCREC. Conclusions: In this child with SDB, the EEG varied with respiratory cycles to a quantifiable extent that changed after adenotonsillectonny. We speculate that RCREC may reflect brief but extremely numerous microarousals.