Topic
Non-rapid eye movement sleep
About: Non-rapid eye movement sleep is a research topic. Over the lifetime, 8661 publications have been published within this topic receiving 389465 citations. The topic is also known as: NREM.
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TL;DR: This work has recorded in the cat the first evidence of neurons whose rhythmic discharge is consistent with the hypothesis of a tonically active neural substrate for rapid eye movement (REM) sleep.
113 citations
TL;DR: The effects of rapid-eye movement (REM) sleep on various mechanisms involved in cardiorespiratory control are of particular significance during the postnatal period since newborns spend much of their time in this sleep state.
Abstract: The cardiorespiratory control system undergoes functional maturation after birth. Until this process is completed, the cardiorespiratory system is unstable, placing infants at risk for cardiorespiratory disturbances, especially during sleep. The profound influence of states of alertness on respiratory and cardiac control has been the focus of intense scrutiny during the last decade. The effects of rapid-eye movement (REM) sleep on various mechanisms involved in cardiorespiratory control are of particular significance during the postnatal period since newborns spend much of their time in this sleep state. In fullterm newborns, REM sleep occupies more than 50% of total sleep time, and this percentage is even greater in preterm newborns. From term to six months of age, the proportion of REM sleep decreases. Since respiratory and cardiac disturbances are known to occur selectively during REM sleep, the predominance of REM sleep may be a risk factor for abnormal sleep-related events during early infancy. Awareness of these developmental changes in sleep patterns is important for clinicians dealing with problems such as apparent life-threatening events (ALTE), sudden infant death syndrome (SIDS), and/or cardiorespiratory responses to respiratory disorders. Our current understanding of respiratory and cardiac control rests mainly on studies conducted during the first months of life. There is a paucity of data on late infancy and early childhood. The present paper will review available data on how sleep affects 1) ventilatory mechanics, in particular of the upper airways and the chest wall; ventilation and apnea; gas exchange; chemoreceptor function; and arousal responses; 2) changes in heart rate and heart rate variability, and the occurrence and mechanisms of bradycardia.
113 citations
TL;DR: The effects of sleep deprivation and caffeine on sustained attention and regional EEG power in waking and sleep were inversely related and suggest that adenosinergic mechanisms contribute to individual differences in waking-induced impairment of neurobehavioral performance and functional aspects of EEG topography associated with sleep deprivation.
Abstract: Large individual differences characterize the changes induced by sleep deprivation on neurobehavioral functions and rhythmic brain activity. To investigate adenosinergic mechanisms in these differences, we studied the effects of prolonged waking and the adenosine receptor antagonist caffeine on sustained vigilant attention and regional electroencephalogram (EEG) power in the ranges of theta activity (6.25–8.25 Hz) in waking and the slow oscillation (
113 citations
TL;DR: The results confirm previous findings that apprehension of a difficult next day is associated with decreased amount of slow wave sleep, increased percentage of stage 2 sleep, bedtime state anxiety and subjectively poor sleep.
113 citations
TL;DR: To improve applicability of automatic sleep staging an efficient subject-independent method is proposed with application in sleep-wake detection and in multiclass sleep staging (awake, non-rapid eye movement (NREM) sleep and rapid eye movement [REM] sleep).
Abstract: To improve applicability of automatic sleep staging an efficient subject-independent method is proposed with application in sleep-wake detection and in multiclass sleep staging (awake, non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep). In turn, NREM is further divided into three stages denoted here by N1, N2, and N3. To assess the method, polysomnographic (PSG) records of 40 patients from our ISRUC-Sleep dataset, which was scored by an expert clinician in the central hospital of Coimbra, are used. To find the best combination of PSG signals for automatic sleep staging, six electroencephalographic (EEG), two electrooculographic (EOG), and one electromyographic (EMG) channels are analyzed. An extensive set of feature extraction techniques are applied, covering temporal, frequency and time-frequency domains. The maximum overlap wavelet transform (MODWT), a shift invariant transform, was used to extract the features in time-frequency domain. The extracted feature set is transformed and normalized to reduce the effect of extreme values of features. The most discriminative features are selected through a two-step method composed by a manual selection step based on features' histogram analysis followed by an automatic feature selector. The selected feature set is classified using support vector machines (SVMs). The system achieved the best performance by combining 6 channels (C3, C4, O1, left EOG (LOC), right EOG (ROC) and chin EMG (X1)) for sleep-wake detection, and 9 channels (C3, C4, O1, O2, F3, F4, LOC, ROC, X1) for multiclass sleep staging.
113 citations