Spectral EEG indicator of pressure to enter into deep sleep: its responsiveness to closing the eyes for just a few minutes exhibits a pure exponential buildup during sleep deprivation

Abstract

According to the two-process model of sleep–wake regulation, a homeostatic sleep pressure, i.e. a pressure to enter into deep non-rapid eyes movement (NREM) sleep, must exhibit a purely exponential buildup during prolonged wakefulness. However, this pressure is usually measured indirectly, i.e. during the following episode of actual deep NREM sleep. The purpose of this paper was to show that, despite a prominent circadian modulation of time course of any waking EEG index, the model-postulated purely exponential buildup of the homeostatic sleep pressure can be directly confirmed. During two days of sleep deprivation experiments, the EEG of healthy adults (N = 30) was recorded every other hour throughout 5-min eyes closed relaxation. Sixteen ln-transformed single-Hz power densities (from 1 to 16 Hz) were computed for each of 5 one-min intervals. Differences between these densities obtained for the first and the following intervals were calculated and averaged. The obtained 16 values were used as the frequency weighting curve for weighting densities of each set of 16 single-Hz power densities. Summing-up of these weighted densities provided a single measure that was found to co-vary with self-rated sleepiness throughout two-day interval of sleep deprivation, thus reflecting the joint influence of the circadian and homeostatic processes. However, two-day time course of responsiveness of this measure to closing the eyes for just a few minutes exhibited a purely exponential buildup. It was concluded that this result provided a direct experimental confirmation of the model-predicted exponential buildup of the homeostatic sleep pressure across prolonged episode of wakefulness.

Keywords:

• Sleep–wake cycle
• two-process model
• EEG spectrum
• sleep deprivation
• sleep pressure

Acknowledgments

I would like to thank Olga G. Donskaya for her help in collecting and preliminary analysis of the EEG recordings.