(Neurology Thematic Programme of the Semmelweis University)
(the Institute of Behavioural Sciences, Semmelweis University)
Features of sleep were shown to reflect aging, typical sex differences and cognitive
abilities of humans. However, these measures are characterized by redundancy and arbitrariness.
Our present approach relies on the assumptions that the spontaneous human brain activity
as reflected by the scalp-derived electroencephalogram (EEG) during non-rapid eye
movement (NREM) sleep is characterized by arrhythmic, scale-free properties and is
based on the power law scaling of the Fourier spectra with the additional consideration
of the rhythmic, oscillatory waves at specific frequencies, including sleep spindles.
Measures derived are the spectral intercept and slope, as well as the maximal spectral
peak amplitude and frequency in the sleep spindle range, effectively reducing 191
spectral measures to 4, which were efficient in characterizing known age-effects,
sex-differences and cognitive correlates of sleep EEG. Future clinical and basic studies
are supposed to be significantly empowered by the efficient data reduction provided
by our approach.
