A critical analysis of quantitative pharmaco-electroencephalography begins with parametrization into variables. The determination of frequency bands according to clinical criteria should be reconsidered. Alternatives may be the determination of factor scores or the definition of frequency bands based on factor analysis. If the latter procedure is used, the clinical alpha-band is subdivided into a lower (alpha 1F = 8,5-10.5 HZ) and an upper (alpha 2F = 10.5-12.5 HZ) part. Furthermore parts of the clinical theta-band (and the delta-band are combined into the delta F-band (1.5-6.0 HZ), for awake healthy volunteers with an occipital alpha-rhythm. Existing concepts of vigilance for the awake stages are not contradictory to the following observations: the factor structure of EEG relative power spectrum variables shows a negative correlation of slow alpha-frequencies with those in the delta F- and beta 3F-band. There is also a negative correlation between slow and fast alpha-wave relative power values.

There is evidence for two types of sleep spindle activity, one with a frequency of about 12 cycles/s (cps) and the other of about 14 cps. Visual examination indicates that both spindle types occur independently, whereby the 12-cps spindles are more pronounced in the frontal and the 14-cps spindles in the parietal region. The purpose of this paper is to provide more information about the exact topography of these patterns. First the occurrence of distinct signals in anterior and posterior brain regions was verified using pattern recognition techniques based on matched filtering. Thus the existence of two distinct sources of activity located in the frontal and parietal region of the brain, respectively, was demonstrated using EEG frequency mapping. Evaluation of sleep recordings showed high stability both in the frequency and location of the presumed spindle generators across sleep. Pharmacological effects of lormetazepam and zopiclone on both spindle types were investigated. Both substances enhanced the sleep spindle activity recorded from the frontal and parietal electrodes, but this increase was more pronounced in the parietal brain region.

From the ECG trace of the sleep polygrams the heart rate Yariability (HRW is derived by determining the intervals between the R peaks, During sleep, transitions of heart rate can be observed in HRV signal which are relevant to changes in the autonomic regulation. For better insight into the vegetative background of sleep, the spectrum analysis of HRV signal and its parameters has been calculated. In particular LF/HF (ratio between the powers in low frequency range over high frequency range) is considered the measure of ympathohagal balance. A trend of such a ratio is compared with the parameters obtained in differen t vegetative tests (Rest to Stand, Valsalva-maneuver etc.). The HR V signal showed characteristic patterns in the time and in the frequency domains in the different sleep states. The presented method offers a pronounced sensibility to explore the autonomic regulatory activities during sleep. IntroductionIn man, alterations of Autonomic Nervous System ( A N S ) function has generally been studied by means of measuring the functional state of different end organs, in particular of the heart. A number of simple tests based on cardiovascular refleses have been developed and used especially in individuals with diabetis mellitus. Most of these tests are provoking a tachycardiac transient episode followed by a bradycardiac one. Determining the ratio of the longest over the shortest R-R interval a crude quantification of the autonomic regulation capacities can be obtained. In 'steady state' condition also spontaneous fluctuations of the heart rate can be observed in man. Many authors have tried to extract more detailed information from the beat to beat oscillations of the heart rate, the goal being to obtain a marker of the 'tonic' regulation activities of both the sympathetic and parasympathetic (or vagal) nervous system. In several papers the sympatho-vagal balance obtained from the power spectral density (PSD) of the heart rate variability (HRV, R-R interval series, as detected from 02'76-6547/93 $3.00 0 1993 IEEE an ECG lead) has been widely demonstrated. Three main frequency components have been established by the current literature, in accordance with various hypothesis on their physiological meaning:The 'respiratory' rhythm of heart period variatiqn, defined as high-frequency (HF) spectral component, is generally considered a marker of vagal modulation;the rhythm corresponding to vasomotor waves and present in heart period and arterial pressure (also referred to as Mayer waves) variabilities, defined as the low-frequency (LF) component, is always increased in condition of sympathetical stimulation and therefore is considered a prevalent marker of sympathetic activation; the very low frequency (VLF) component, due probably to long term regulatory mechanisms, such as humoral factors, temperature and other slow components; a reciprocal relation exists between the power of the LF and HF rhythms, that is similar to that characterising the sympatho-vagal balance: it is then possible to quantify such balance ...

Previous attempts at automated analysis of sleep were mainly directed towards imitating the Rechtschaffen and Kales rules (RKR) in order to save scoring time and further objectify the procedure. RKR, however, do not take into consideration the sleep microstructure of REM, stage 2, and SWS. While the microstructure of stage 2 has been analyzed in the past decade, the microstructure of REM and SWS are virtually unknown. In stage 2 the amount and distribution of spindles, K complexes, and arousal reactions have been studied. At least two types of spindles (12/s and 14/s) with different dynamics and locations have been identified. Two different shapes for K complexes have been described: one related to external sensory stimuli with similarities to evoked potentials and another one more related to sinusoidal slow wave activity seen in SWS. These two different K complex shapes have different distributions and, obviously, different functions. The authors also suggest that one should differentiate between arousal reactions and true arousals. Recent investigations suggest two types of delta waves in SWS. The more sinusoidal 1-3/s delta waves with a frontal maximum are already seen with lower amplitude in late stage 2 and increase their amplitude and incidence towards stage 3 and Stage 4. The other delta-wave type is slower (< 1/s), polymorphic, and has varying amounts of theta and higher frequency waves superimposed. During REM sleep it seems to be important to separate phases with rapid eye movements from those with none (REM sine REM), and count the amount and distribution of sawtooth activity. Background activity during REM and REM sine REM, as well as intra- and interhemispheric coherence should be analyzed separately. Only if the microstructure of the sleep EEG can be analyzed automatically using newer techniques such as transformation into wavelets and pattern classification with neuronal networks, and only if we learn more about the importance of microstructure elements, can automated sleep analysis go beyond the limited information obtained from scoring according to RKR.

In a pilot double-blind trial in 21 patients with learned or idiopathic insomnia (DSM-IIIR), patients received placebo for 1 week (nights 1-7), either active (zolpidem, 10 mg) or placebo treatment for 2 weeks (nights 8-21) and then placebo for a further week (nights 22-28). Variables to measure efficacy, rebound and withdrawal were assessed daily from day 1 to day 28. Polysomnographic recordings together with sleep cycle analysis were performed on nights 7, 21 and 28. Patients treated with 10 mg zolpidem for 2 weeks had significantly improved sleep efficiency at the end of the randomised double-blind phase compared with the placebo group. Fractionated sleep-cycle analysis showed an increase in slow-wave sleep during the first 2-hour cycle after sleep onset. During the withdrawal placebo week, most of the main sleep variables remained relatively stable in the zolpidem group (nights 22-28), and deteriorated further in the placebo group. At the end of the withdrawal phase, there was a statistically significant difference between groups, in favour of the zolpidem treatment, in sleep efficiency, total sleep time, absolute and percentage of time awake, and percentage of REM sleep. REM sleep, which was normal in both groups at baseline, decreased significantly in the placebo group between nights 22 and 28 (during the withdrawal placebo week) compared with the zolpidem treatment group, and the number of periods of time awake increased. Minor subjective complaints were recorded under zolpidem and were comparable with those under placebo. Zolpidem seemed to improve some important sleep variables, when assessed both objectively and subjectively. The sleep cycle analysis suggested a possible shift of slow-wave sleep to an earlier period of the night, with a more physiological sleep structure. There was no evidence for withdrawal or rebound after stopping the 2 weeks of zolpidem treatment, but rather signs that the effect of zolpidem outlasted active treatment. The present pilot study justifies a prospective confirmatory comparison of zolpidem with benzodiazepines in an adequate number of patients and withdrawal after 6-8 weeks of treatment.