Sleep EEG composition in the first three months of life in monozygotic and dizygotic twins.

We investigated genetic influence on sleep electroencephalogram (EEG) composition by a classical twin study of monozygotic (MZ) and dizygotic (DZ) twins in the first 3 months of life. Polysomnographic (PSG) recordings were obtained in 10 MZ and 20 DZ twin pairs in the 37th, 46th, and 52nd week of postmenstrual age (PMA). The EEG power spectra were generated on the basis of fast Fourier transformation (FFT). Genetic influence on active sleep/rapid eye movement (AS/REM)] and quiet sleep/non rapid eye movement (QS/NREM) sleep composition was estimated by calculating within pair concordance and the intraclass correlation coefficients (ICCs) for delta (0.5-3.5 Hz), theta (4-7.5 Hz), alpha (8-11.5 Hz), sigma (12-14 Hz), and beta (14.5-20 Hz) at central derivation. MZ twins show higher ICCs than DZ twins for alpha, sigma, and beta spectral powers during QS/NREM sleep in the 37th, 46th, and 52nd week PMA. However, there was no significant difference (P > .05) between the 2 types of twins in absolute differences of EEG spectral power of the alpha, beta, and sigma frequency ranges in the 37th, 46th, and 52nd week PMA. The greatest mean absolute difference within MZ and DZ twin pairs and also between MZ and DZ twin groups was identified in the delta frequency range. Our findings gave an indication of genetic influence on alpha, sigma, and beta frequency ranges in the QS/NREM sleep stage.

Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal.

This study presents a novel approach for the electroencephalogram (EEG) signal quantification in which the empirical mode decomposition method, a time-frequency method designated for nonlinear and non-stationary signals, decomposes the EEG signal into intrinsic mode functions (IMF) with corresponding frequency ranges that characterize the appropriate oscillatory modes embedded in the brain neural activity acquired using EEG. To calculate the instantaneous frequency of IMFs, an algorithm was developed using the Generalized Zero Crossing method. From the resulting frequencies, two different novel features were generated: the median instantaneous frequencies and the number of instantaneous frequency changes during a 30s segment for seven IMFs. The sleep stage classification for the daytime sleep of 20 healthy babies was determined using the Support Vector Machine classification algorithm. The results were evaluated using the cross-validation method to achieve an approximately 90% accuracy and with new examinee data to achieve 80% average accuracy of classification. The obtained results were higher than the human experts' agreement and were statistically significant, which positioned the method, based on the proposed features, as an efficient procedure for automatic sleep stage classification. The uniqueness of this study arises from newly proposed features of the time-frequency domain, which bind characteristics of the sleep signals to the oscillation modes of brain activity, reflecting the physical characteristics of sleep, and thus have the potential to highlight the congruency of twin pairs with potential implications for the genetic determination of sleep.