The coupling dynamics of two time series can be assessed by pointwise transinformation (PTI). Due to its high temporal resolution, this algorithm is ideal for analysis of sleep microstructure. Different types of electroencephalographic (EEG) activation phases, like single K-complexes, K-complexes associated with spindle or alpha activity, K-complexes mixed with delta waves, and arousals, can be detected and changes in EEG coupling can be quantified.
Nine hundred ninety-one one-minute EEG segments (C3-A2/C4-A1) containing the described types of activation phases were selected from the sleep EEGs of 12 healthy persons. PTI was calculated with 250 Hz resolution and an embedding dimension of 20. An averaged PTI curve was assessed for single K-complexes and K-complexes followed by spindle and alpha activity, respectively.
During background activity, PTI was nearly 0. With the onset of a K-complex, PTI increased significantly in a sequence of distinct phases (rising - peak - decay). For single K-complexes, the PTI curve had a nearly symmetric dome-shaped form. The decay phase was prolonged by subsequent spindle or alpha activity. In K-complexes mixed with delta activity and in arousals, repetitive maxima of PTI were obtained. The durations of arousals and their coupling phases were correlated (r=0.83).
PTI displays the coupling dynamics of the sleep EEG with high resolution. It detects phases of activation represented by single K-complexes and various types of arousals. These induce a specific run of the PTI curve clearly distinguishable from background activity. PTI might, therefore, prove useful in the analysis of sleep microstructure.
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