EEG arousals were quantified in 40 nocturnal polysomnographic recordings belonging to four age groups (teenagers: 10 to 19 years; young adults: 20 to 39 years; middle-aged: 40 to 59 years; elderly: > or = 60 years). Ten subjects (five males and five females) participated in each group. The subjects were healthy and sound sleepers. All sleep recordings were preceded by an adaptation night which ...

This paper investigates the automated detection of a patient’s breathing rate and heart rate from their skin conductivity as well as sleep stage scoring and breathing event detection from their EEG. The software developed for these tasks is tested on data sets obtained from the sleep disorders unit at the Adelaide Women’s and Children’s Hospital. The sleep scoring and breathing event detection ...

Permutation entropy and order patterns in an EEG signal have been applied by several authors to study sleep, anesthesia, and epileptic absences. Here, we discuss a new version of permutation entropy, which is interpreted as distance to white noise. It has a scale similar to the well-known χ2 distributions and can be supported by a statistical model. Critical values for significance are provided...

Sleep is generally categorized into discrete stages based on characteristic electroencephalogram (EEG) patterns. This traditional approach represents sleep architecture in a static way, but it cannot reflect variations in sleep across time and across the cortex. To investigate these dynamic aspects of sleep, we analyzed sleep recordings in 14 healthy volunteers with a novel, frequency-based EEG...

A particular property of the sleeping brain is that it exhibits dynamics on very different time scales ranging from the typical sleep oscillations such as sleep spindles and slow waves that can be observed in electroencephalogram (EEG) segments of several seconds duration over the transitions between the different sleep stages on a time scale of minutes to the dynamical processes involved in sl...

Electroencephalograph (EEG) analysis enables the dynamic behavior of the brain to be examined. If the behavior is nonlinear then nonlinear tools can be used to glean information on brain behavior, and aid in the diagnosis of sleep abnormalities such as obstructive sleep apnea syndrome (OSAS). In this paper the sleep EEGs of a set of normal children and children with mild OSAS are evaluated for ...

This paper focuses on the problem of selecting relevant features extracted from human polysomnographic (PSG) signals to perform accurate sleep/wake stages classification. Extraction of various features from the electroencephalogram (EEG), the electro-oculogram (EOG) and the electromyogram (EMG) processed in the frequency and time domains was achieved using a database of 47 night sleep recording...

Respiratory effort has been widely used for objective analysis of human sleep during bedtime. Several features extracted from respiratory effort signal have succeeded in automated sleep stage classification throughout the night such as variability of respiratory frequency, spectral powers in different frequency bands, respiratory regularity and self-similarity. In regard to the respiratory ampl...

Pharmaco-sleep studies in humans aim at the description of the effects of drugs, most frequently substances that act on the central nervous system, by means of quantitative analysis of biosignals recorded in subjects during sleep. Up to 2007, the only standard for the classification of sleep macrostructure that found worldwide acceptance were the rules published in 1968 by Rechtschaffen and Kal...

Physiological and/or pathological implications of the dynamics of sleep stage transitions have not, to date, been investigated. We report detailed duration and transition statistics between sleep stages in healthy subjects and in others with chronic fatigue syndrome (CFS); in addition, we also compare our data with previously published results for rats. Twenty-two healthy females and 22 female ...