Objective Whether there are age-related changes in slow wave activity (SWA) rise time, a marker of homeostatic sleep drive, is unknown. Additionally, although sleep medication use is highest among older adults, the quantitative electroencephalographic (EEG) profile of the most commonly prescribed sleep medication, zolpidem, in older adults is also unknown. We therefore quantified age-related and regional brain differences in sleep EEG with and without zolpidem. Methods Thirteen healthy young adults aged 21.9 ± 2.2 years and 12 healthy older adults aged 67.4 ± 4.2 years participated in a randomized, double-blind, within-subject study that compared placebo to 5 mg zolpidem. Results Older adults showed a smaller rise in SWA and zolpidem increased age-related differences in SWA rise time such that age differences were observed earlier after latency to persistent sleep. Age-related differences in EEG power differed by brain region. Older, but not young, adults showed zolpidem-dependent reductions in theta and alpha frequencies. Zolpidem decreased stage 1 in older adults and did not alter other age-related sleep architecture parameters. Conclusions SWA findings provide additional support for reduced homeostatic sleep drive or reduced ability to respond to sleep drive with age. Consequences of reduced power in theta and alpha frequencies in older adults remain to be elucidated.

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Huang's Empirical Mode Decomposition (EMD) is an algorithm for analyzing nonstationary data that provides a localized time-frequency representation by decomposing the data into adaptively defined modes. EMD can be used to estimate a signal's instantaneous frequency (IF) but suffers from poor performance in the presence of noise. To produce a meaningful IF, each mode of the decomposition must be nearly monochromatic, a condition that is not guaranteed by the algorithm and fails to be met when the signal is corrupted by noise. In this work, the extraction of modes containing both signal and noise is identified as the cause of poor IF estimation. The specific mechanism by which such "transition" modes are extracted is detailed and builds on the observation of Flandrin and Goncalves that EMD acts in a filter bank manner when analyzing pure noise. The mechanism is shown to be dependent on spectral leak between modes and the phase of the underlying signal. These ideas are developed through the use of simple signals and are tested on a synthetic seismic waveform.