Sleep characteristics are candidates for predictive biological markers in patients with severe psychiatric diseases, in particular affective disorder and schizophrenia. The genetic components of sleep determination in humans remain, to a large degree, unelucidated. In particular, the heritability of rapid eye movement (REM) sleep and EEG bursts of oscillatory brain activity in Non-REM sleep, i.e. sleep spindles, are of interest. In addition, recent findings suggest a strong role of distinct sleep spindle types in memory consolidation, making it important to identify sleep spindles in slow wave sleep (SWS) and to separate slow and fast spindle localization in the frequency range. However, predictive sleep biomarker research requires large sample sizes of healthy and affected human individuals. Therefore, the present work addressed two questions. The first aim was to optimize data analysis by developing algorithms that allow an efficient and reliable identification of rapid eye movements (REMs) and sleep EEG spindles. In the second part, developed methods were applied to sleep EEG data from a classical twin study to identify genetic effects on tonic and phasic REM sleep parameters, sleep spindles, and their trait-like characteristics. The algorithm for REM detection was developed for standard clinical two channel electrooculographic montage. The goal was to detect REMs visible above the background noise, and in the case of REM saccades to classify each movement separately. In order to achieve a high level of sensitivity, detection was based on a first derivative of electrooculogram (EOG) potentials and two detection thresholds. The developed REM detector was then validated in n=12 polysomnographic recordings from n=7 healthy subjects who had been previously scored visually by two human experts according to standard guidelines. Comparison of automatic REM detection with human scorers revealed mean correlations of 0.94 and 0.90, respectively (mean correlation between experts was 0.91). The developed automatic sleep spindle detector assessed individualized signal amplitude for each channel as well as slow and fast spindle frequency peaks based on the spectral analysis of the EEG signal. The spindle detection was based on Continuous Wavelet Transform (CWT); it localized the exact length of sleep spindles and was sensitive also for detection of sleep spindles intermingled in high amplitude slow wave EEG activity. The automatic spindle detector was validated in n=18 naps from n=10 subjects, where EEG data were scored both visually and by a commercial automatic algorithm (SIESTA). Comparison of our own spindle detector with results from the SIESTA algorithm and visual scoring revealed the correlations of 0.97 and 0.92, respectively (correlation between SIESTA algorithm and visual scoring was 0.90). In the second part of the work, the similarity of given sleep EEG parameters in n=32 healthy monozygotic (MZ) twins was compared with the similarity in n=14 healthy same-gender dizygotic (DZ) twins. The author of the current work did not participate in acquisition of twin study sample. EEG sleep recordings used for the heritability study were collected and already described by Ambrosius et al. (2008). Investigation of REM sleep included the absolute EEG spectral power, the shape of REM power spectrum, the amount and the structural organization of REMs; parameters of Non-REM sleep included slow and fast sleep spindle characteristics as well as the shape of the Non-REM power spectrum in general. In addition to estimating genetic effects, differences in within-pair similarity and night-to-night stability of given parameters were illustrated by intraclass correlation coefficients (ICC) and cluster analysis. A substantial genetic influence on both spectral composition and phasic parameters of REM sleep was observed. A significant genetic variance in spectral power affected delta to high sigma and high beta to gamma EEG frequency bands, a