Objective: Universal high-resolution time-frequency parameterization of sle
ep EEG structures.
Methods: A new algorithm called Matching Pursuit was used for the decomposi
tion of sleep EEG into waveforms chosen from a large and redundant set of f
unctions. As a result all signal structures were parameterized in terms of
their frequency, time occurrence, time span and energy. Slow wave activity
and sleep spindles were identified according to neurophysiological criteria
and various distributions describing their time evolution, topographical a
nd frequency characteristics were constructed.
Results: Two types of sleep spindles of different topological and spectral
properties were identified. High time-frequency resolution made possible se
paration of superimposed spindles. Cross-correlation between high- and low-
frequency components of superimposed spindles revealed a fixed time-delay b
etween them, the high-frequency component preceding the low-frequency one.
Conclusion: The results of our study suggest that processes of generation o
f both types of sleep spindles are weakly coupled. (C) 1999 Elsevier Scienc
e ireland Ltd. All rights reserved.