Sleep has been proposed to indicate preserved residual brain functioning in patients suffering from disorders of consciousness (DOC) after awakening from coma. However, a reliable characterization of sleep patterns in this clinical population continues to be challenging given severely altered brain oscillations, frequent and extended artifacts in clinical recordings and the absence of establish...

We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge. We used an openly available dataset from 20 healthy young adults for evaluation and applied 20-fold crossvalidation. We used class-balanced random sampling within the stochastic...

Sleep stage classification is of great importance in sleep analysis, which provides information for the diagnosis and monitoring sleep-related conditions. To accurately analyze structure under comfortable conditions, many studies have applied deep learning to staging based on single-lead electrocardiograms (ECGs). However, there still room improvement inter-subject classification. In this paper...

A new approach, based on a Markov transition matrix, is proposed to explain frequent sleep and wake transitions during sleep. The matrix is determined by analyzing hypnograms of 113 obstructive sleep apnea patients. Our approach shows that the statistics of sleep can be constructed via a single Markov process and that durations of all states have modified exponential distributions, in contrast ...

Background and Objectives: Sleep is a sign of the greatness of God. In this article sleep is described from Quran and modern health sciences. Methods: This is a qualitative systematic review. Data were gathered from the Quran, related Islamic narratives and literatures. Words that were searched included sleep,sleep stages, Subaat, Hojoo, Ruqood, Nu’ass, Sinah. Results: The results s...

Sleep staging has an important role in diagnosing sleep disorders. It is usually done by a sleep expert through examining sleep Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG) signals of the patients and determining the stages of sleep in different time sections named as epochs. Manual sleep staging is preferred among the sleep experts but because it is rather tiring an...

An alternative DSS which models the behaviour of the Heart Rate Variability (HRV) signal linked to stable (NREM) and instable (REM) cerebral waves during sleep and a probabilistic model of the sleep stages transitions for decision was developed. Time-Varying Autoregressive Models (TVAMs) were used as feature extractor while Hidden Markov Models (HMM) was used as time series classifier. 24 full ...

Recently, many lines of investigation in neuroscience and statistical physics have converged to raise the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG) signals is nonlinear, with self-affine dynamics, while scalp-recorded EEG signals themselves are nonstationary. Therefore, traditional methods of EEG analysis may miss many properties...

This paper presents a fusion-based neural network (NN) classification algorithm for 40-Hz auditory steady state response (ASSR) ensemble averaged signals which were recorded from eight human subjects for observing sleep patterns (wakefulness W0 and deep sleep N3 or slow wave sleep SWS). In SWS, sensitivity to pain is the lowest relative to other sleep stages and arousal needs stronger stimuli. ...

EEG coherence was examined in relation to four measures of socioemotional dream content, including a new measure--the proportional representation of a character's face. Twenty-four healthy subjects, recorded for sleep stages and EEG activity, were awakened from REM sleep to report dream mentation and to rate it on these variables. Coherence scores were calculated for homologous interhemispheric...