The goal of this study is to contribute to discussions about age-related changes in electroencephalogram (EEG) complexity. Eight characteristics of complexity were evaluated for sleep EEG of 175 healthy subjects. The complexity of the sleep EEG significantly increased up to the age of about 60 years. Over 60 years, the complexity stagnated or slightly decreased. The same tendencies were manifes...

The sleeping neocortex shows nested oscillatory activity in different frequency ranges, characterized by fluctuations between "up-states" and "down-states." High-density neuronal ensemble recordings in rats now reveal the interaction between synchronized activity in the hippocampus and neocortex: Electroencephalographic sharp waves in the hippocampus were more probable during down-states than d...

OBJECTIVE Evaluate the efficacy of ramelteon, an MT/1MT2-receptor agonist, for the treatment of transient insomnia in healthy adults. DESIGN Randomized, double-blind, placebo-controlled design using a model of transient insomnia related to sleeping in a novel environment. SETTING Fourteen sleep research centers. PARTICIPANTS Healthy adults (N=375; 228 women), aged 35 to 60 years, who had ...

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to i...

In this paper we report a detection method for different sleep stages and it is based on a single-channel electroencephalogram (EEG) system. The system is simple and can be easily setup in homes to perform sleep EEG recording, overnight sleep EEG automatic staging, and sleep quality evaluation. EEG data of 14 sleeping subjects were recorded through the entire night. All subjects were within the...

This work proposes a methodology for sleep stage classification based on two main approaches: the combination of features extracted from electroencephalogram (EEG) signal by different extraction methods, and the use of stacked sequential learning to incorporate predicted information from nearby sleep stages in the final classifier. The feature extraction methods used in this work include three ...

In this paper, we present a method that can be used to automatically classify sleep states in an all-night polysomnogram (PSG) to generate a hypnogram for the assesment of sleep-related disorders. The method is based on ideas of segmentation and classification (clustering) using sleep related features. Segments are clustered to generate groups of similar patterns that can subsequently be labele...