Our results establish the face validity and convergent validity of ASEEGA for single-channel sleep analysis in healthy individuals. ASEEGA appears as a good candidate for diagnostic aid and automatic ambulant scoring.

The relationship between behavioral state, discharge pattern, and discharge rate was investigated in 26 lateral geniculate nucleus (LGN) units recorded in cats in the dark during waking (W), synchronized sleep (S), and desynchronized sleep (D). A distinctive state-dependent discharge pattern was the presence of stereotyped bursts of 2-7 spikes that occurred in 63% of the units. These bursts were most frequent in S, much less frequent in D, and rarely occurred in W. Lack of association with discharge rate changes between states showed the bursting to be a true state-dependent phenomenon. A burst consisted of 2-7 spikes, with each successive interspike interval being longer than the preceding one; in the 200 ms prior to burst occurrence, discharge probability decreased markedly. This structure of burst organization suggested a model of generation wherein each burst was caused by a unitary event of varying intensity, perhaps a rebound following a hyperpolarization. Spectral and autocorrelational analyses showed bursts occurred rhythmically in three cells at a frequency of 3-4 Hz and in two cells at a frequency of 10-12 Hz, indicating a possible linkage with slow-wave generators. While the number of bursts in the various behavioral states was a state-dependent phenomena, other aspects of discharge pattern were shown to be rate dependent. To evaluate discharge pattern apart from the occurrence of bursts, a "primary event spike train" was formed; this consisted of individual spikes and the first spike of each burst. This analysis showed that, within S, the probability of burst occurrence was highest when the primary spike rate was low. Quantitative analyses showed that first-order pattern measures (the form of the interspike interval histogram, IH) were dependent on the mean interspike interval (ISI, the inverse of mean rate). This association explained 83-89% of the variance in a power series approximation of IH form. Joint interval histograms (JIH) were used to evaluate the signature of bursts and of the form of the primary spike train. As with interval histograms, the main features of the form of the primary spike JIH were dependent on the primary spike rate. Thus, we concluded that first- and second-order discharge patterns of primary events were rate dependent and not state dependent. Our data are compatible with a model where in the absence of retinal input, the frequency of LGN primary spikes over behavioral state changes is largely determined by brain stem reticular formation input.(ABSTRACT TRUNCATED AT 400 WORDS)

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