Nocturnal Psychophysiological Correlates of Somatic Conditions and Sleep Disorders

1975 ◽  
Vol 6 (1-2) ◽  
pp. 43-62 ◽  
Author(s):  
Joyce D. Kales ◽  
Anthony Kales
Keyword(s):  
Sleep Disorders ◽  
Sleep Stage ◽  
Symptomatic Relief ◽  
Nrem Sleep ◽  
Normal Subjects ◽  
Sleep Stages ◽  
Sleep Laboratory ◽  
Drug Induced ◽  
Night Terrors ◽  
Psychophysiological Correlates

Modern sleep research studies have provided the practicing physician with considerable new information concerning the basic psychophysiology of sleep, the effects of medical conditions on sleep and the role of maturational and emotional factors in producing certain sleep disorders. Medical and psychiatric disorders, sleep disorders and drug-induced sleep stage alterations are studied in the sleep laboratory using the same techniques developed to analyze sleep patterns in normal subjects. After initial sleep laboratory adaptation, a profile of the sleep characteristics of various clinical conditions is obtained. This profile can be compared to sleep profiles of normal subjects as well as to the effects on sleep of subsequent experimental or therapeutic procedures. Various studies have shown that coronary artery, duodenal ulcer and nocturnal headache patients experience angina, increased gastric acid secretion and migraine or cluster headaches, respectively during REM sleep. Adult nocturnal asthmatic episodes occur out of all sleep stages while attacks of dyspnea in asthmatic children occur in all stages except stage 4 sleep. Hypothyroid patients show decreases in stages 3 and 4 sleep, while in hyperthyroid patients the percentage of time spent in stages 3 and 4 sleep is markedly increased. Enuretic episodes occur predominantly in non-rapid eye movement (NREM) sleep. Sleepwalking and night terror episodes occur exclusively out of NREM sleep, particularly from stages 3 and 4 sleep. Most child somnambulists and children with night terrors “outgrow” this disorder, suggesting a delayed maturation of the central nervous system. Stimulant drugs are effective in the treatment of the sleep attacks of narcolepsy and in treating certain cases of hypersomnia, while imipramine is an effective treatment for the auxiliary symptoms of narcolepsy. Psychological disturbances are frequent in adult somnambulism and night terrors as well as in hypersomnia and insomnia. Proper pharmacologic treatment to provide symptomatic relief for insomnia is recommended to enhance the psychotherapeutic process.

scholarly journals Primary sleep enuresis in childhood: polysomnography evidences of sleep stage and time modulation

1993 ◽  
Vol 51 (1) ◽  
pp. 41-45 ◽  
Author(s):  
Rubens Reimäo ◽  
Laércio C. Pachelli ◽  
Ricardo Carneiro ◽  
Guido Faiwichow
Keyword(s):  
Sleep Disorders ◽  
Normal Distribution ◽  
Sleep Stage ◽  
Nrem Sleep ◽  
Sleep Stages ◽  
Recording Time ◽  
Time Modulation ◽  
Drug Free ◽  
Sleep Cycles

The objective of this study was to evaluate enuretic events and its relations to sleep stages, sleep cycles and time durations in a selected group of children with primary essential sleep enuresis. We evaluated 18 patients with mean age of 8.2 years old (ranging from 5 to 12 years); 10 were males and 8 females (n.s.). They were referred to the Sleep Disorders Center with the specific complaint of enuresis since the first years of life (primary). Pediatric, urologic and neurologic workup did not show objective abnormalities (essential). The standard all-night polysomnography including an enuresis sensor attached to the shorts in the crotch area was performed. Only enuretic events nights were included. All were drug free patients for two weeks prior to polysomnography. In this report, only one polysomnography per patient was considered. The enuretic events were phase related, occurring predominantly in non-REM (NREM) sleep (p<0.05). There was no predominance of enuretic events among the NREM stages (n.s.). A tendency of these events to occur in the first two sleep cycles was detected but may be due to the longer duration of these cycles. The events were time modulated, adjusted to a normal distribution with a mean of 213.4 min of recording time.

Metabolic rate and breathing during sleep

1985 ◽  
Vol 59 (2) ◽  
pp. 384-391 ◽  
Author(s):  
D. P. White ◽  
J. V. Weil ◽  
C. W. Zwillich
Keyword(s):  
Metabolic Rate ◽  
Eye Movement ◽  
Sleep Stage ◽  
Minute Ventilation ◽  
Respiratory Control ◽  
Normal Subjects ◽  
Co2 Production ◽  
Sleep Stages ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep

Recent investigation suggests that both ventilation (VE) and the chemical sensitivity of the respiratory control system correlate closely with measures of metabolic rate [O2 consumption (VO2) and CO2 production (VCO2)]. However, these associations have not been carefully investigated during sleep, and what little information is available suggests a deterioration of the relationships. As a result we measured VE, ventilatory pattern, VO2, and VCO2 during sleep in 21 normal subjects (11 males and 10 females) between the ages of 21 and 77 yr. When compared with values for awake subjects, expired ventilation decreased 8.2 +/- 2.3% (SE) during sleep and was associated with a 8.5 +/- 1.6% decrement in VO2 and a 12.3 +/- 1.7% reduction in VCO2, all P less than 0.01. The decrease in ventilation was a product primarily of a significant decrease in tidal volume with little change in frequency. None of these findings were dependent on sleep stage with results in rapid-eye-movement (REM) and non-rapid-eye-movement sleep being similar. Through all sleep stages ventilation remained tightly correlated with VO2 and VCO2 both within a given individual and between subjects. Although respiratory rhythmicity was somewhat variable during REM sleep, minute ventilation continued to correlate with VO2 and VCO2. None of the parameters described above were influenced by age or gender, with male and female subjects demonstrating similar findings. Ten of the subjects demonstrated at least occasional apneas. These individuals, however, were not found to differ from those without apnea in any other measure of ventilation or metabolic rate.

Partitioning of inhaled ventilation between the nasal and oral routes during sleep in normal subjects

2003 ◽  
Vol 94 (3) ◽  
pp. 883-890 ◽  
Author(s):  
Michael F. Fitzpatrick ◽  
Helen S. Driver ◽  
Neela Chatha ◽  
Nha Voduc ◽  
Alison M. Girard
Keyword(s):  
Eye Movement ◽  
Slow Wave ◽  
Sleep Stage ◽  
Minute Ventilation ◽  
Normal Subjects ◽  
Sleep Stages ◽  
Rapid Eye Movement ◽  
Nasal Mask ◽  
Rapid Eye Movement Sleep ◽  
Significant Difference

The oral and nasal contributions to inhaled ventilation were simultaneously quantified during sleep in 10 healthy subjects (5 men, 5 women) aged 43 ± 5 yr, with normal nasal resistance (mean 2.0 ± 0.3 cmH2O · l−1 · s−1) by use of a divided oral and nasal mask. Minute ventilation awake (5.9 ± 0.3 l/min) was higher than that during sleep (5.2 ± 0.3 l/min; P < 0.0001), but there was no significant difference in minute ventilation between different sleep stages ( P = 0.44): stage 2 5.3 ± 0.3, slow-wave 5.2 ± 0.2, and rapid-eye-movement sleep 5.2 ± 0.2 l/min. The oral fraction of inhaled ventilation during wakefulness (7.6 ± 4%) was not significantly different from that during sleep (4.3 ± 2%; mean difference 3.3%, 95% confidence interval −2.1–8.8%, P = 0.19), and no significant difference ( P = 0.14) in oral fraction was observed between different sleep stages: stage two 5.1 ± 2.8, slow-wave 4.2 ± 1.8, rapid-eye-movement 3.1 ± 1.7%. Thus the inhaled oral fraction in normal subjects is small and does not change significantly with sleep stage.

scholarly journals 1205 Activity Trackers As A Tool In Sleep Research: Determining Discrepancies In Trackers Vs. PSG

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A460-A461
Author(s):  
E P Pollet ◽  
D P Pollet ◽  
B Long ◽  
A A Qutub
Keyword(s):  
Sleep Disorders ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Sleep Efficiency ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Sleep Study ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Large Populations

Abstract Introduction Fitness-based wearables and other emerging sensor technologies have the potential to track sleep across large populations longitudinally in at-home environments. To understand how these devices can inform research studies, limitations of available trackers need to be compared to traditional polysomnography (PSG). Here we assessed discrepancies in sleep staging in activity trackers vs. PSG in subjects with various sleep disorders. Methods Twelve subjects (age 41-78, 7f, 5m) wore a Fitbit Charge 3 while undergoing a scheduled sleep study. Six subjects had been previously diagnosed with a sleep disorder (5 OSA, 1 CSA). 4 subjects used CPAP throughout the night, 2 had a split night (CPAP 2nd half of the night), and 6 had a PSG only. Activity tracker staging was compared to 2 RPSGTs staging. Results Of the 12 subjects, eight subjects’ sleep was detected in the activity tracker, and compared across sleep stages to the PSG (7 female, 1 male, ages 41-78, AHI 0.3-87, RDI 0.5-94.4, sleep efficiency 74%+/-18, 4 PSG, 1 split, 3 CPAP). The activity tracker matched either tech 52% (+/- 13). The average difference in score tech and activity tracker staging for sleep onset (SO) was 16 +/- 15 minutes and wake after sleep onset was 43.5 +/- 44 minutes. Sensitivity, specificity, and balanced accuracy were found for each sleep stage. Respectively, Wake: 0.45+/-0.27, 0.97+/-0.03, 0.71+/-0.12, REM: 0.41+/-0.30, 0.90+/-0.06, 0.60+/-0.28, Light: 0.71+/-0.09, 0.58+/-0.19, 0.65+/-0.10, Deep: 0.63+/-0.52, 0.88+/-0.05, 0.59+/-0.49. Conclusion From this study of 12 subjects seen at a sleep clinic for suspected sleep disorders, activity trackers performed best in wake, REM and deep sleep specificity (&gt;=88%), while they lacked sensitivity to REM and wake (&lt;=45%) stages. The tracker did not detect sleep in 4 subjects who had elevated AHI or low sleep efficiency. Further analysis can identify whether discrepancies between the Fitbit and PSG can be predicted by distinct patterns in sleep staging and/or identify subject exclusion criteria for activity tracking studies. Support This project in on-going with the support of Academy Diagnostics Sleep and EEG Center and staff.

scholarly journals Shallow Classifier with Sampling for Sleep Stage Classification of Autism Patients

2018 ◽  
Vol 7 (4.44) ◽  
pp. 194
Author(s):  
Intan Nurma Yulita ◽  
Mohamad Ivan Fanany ◽  
Aniati Murni Arymurthy
Keyword(s):  
Sleep Disorders ◽  
Nearest Neighbor ◽  
Sleep Stage ◽  
The Other ◽  
Sleep Stages ◽  
K Nearest Neighbor ◽  
Class Imbalance Problem ◽  
Other Hand ◽  
Stage Classification ◽  
Sleep Stage Classification

Autism is a brain development disorder that affects the patient's ability to communicate and interact with others. Most people with autism get sleep disorders. But they have some difficulty to communicate, so this problem is getting worse. The alternative that can be done is to detect sleep disorders through polysomnography. One of the test purposes is to classify the sleep stages. The doctors need a long time to process it. This paper presents an automatic sleep stage classification. The classification was based on the shallow classifiers, namely naive Bayes, k-nearest neighbor (KNN), multi-layer perceptron (MLP), and C4.5 (a type of decision tree). On the other hand, this dataset has a class imbalance problem. As a solution, this study carried out the mechanism of resampling. The results show that the use of d as a measure of the uniformity of data distribution greatly influenced the classification performance. The higher d, the more uniform the distribution of data (0 <= d <= 1). The performance with d = 1 was higher than d = 0. On the other hand, KNN was the best classifier. The highest accuracy and F-measure were 83.07 and 82.80 respectively. 

scholarly journals Ketamine and sleep modulate neural complexity dynamics in cats

2021 ◽  
Author(s):  
Claudia Pascovich ◽  
Santiago Castro-Zaballa ◽  
Pedro A.M. Mediano ◽  
Daniel Bor ◽  
Andrés Canales-Johnson ◽  
...  
Keyword(s):  
Response Curve ◽  
Sleep Stage ◽  
Nrem Sleep ◽  
Conscious State ◽  
Sleep Stages ◽  
Highly Sensitive ◽  
Level Of Consciousness ◽  
State Changes ◽  
Intracranial Electrodes ◽  
Different Levels

There is increasing evidence that level of consciousness can be captured by neural informational complexity: for instance, complexity, as measured by the Lempel Ziv (LZ) compression algorithm, decreases during anesthesia and non-rapid eye movement (NREM) sleep in humans and rats, when compared to LZ in awake and REM sleep. In contrast, LZ is higher in humans under the effect of psychedelics, including subanesthetic doses of ketamine. However, it is both unclear how this result would be modulated by varying ketamine doses, and whether it would extend to other species. Here we studied LZ with and without auditory stimulation during wakefulness and different sleep stages in 5 cats implanted with intracranial electrodes, as well as under subanesthetic doses of ketamine (5, 10, and 15 mg/kg i.m.). In line with previous results, LZ was lowest in NREM sleep, but similar in REM and wakefulness. Furthermore, we found an inverted U-shaped curve following different levels of ketamine doses in a subset of electrodes, primarily in prefrontal cortex. However, it is worth noting that the variability in the ketamine dose-response curve across cats and cortices was larger than that in the sleep-stage data, highlighting the differential local dynamics created by two different ways of modulating conscious state. These results replicate previous findings, both in humans and other species, demonstrating that neural complexity is highly sensitive to capture state changes between wake and sleep stages while adding a local cortical description. Finally, this study describes the differential effects of ketamine doses, replicating a rise in complexity for low doses, and further fall as doses approach anesthetic levels in a differential manner depending on the cortex.

Increased Chin Muscle Tone during All Sleep Stages in Children Taking SSRI Antidepressants and in Children with Narcolepsy Type 1

SLEEP ◽  
2021 ◽  
Author(s):  
Raffaele Ferri ◽  
Maria P Mogavero ◽  
Oliviero Bruni ◽  
Giuseppe Plazzi ◽  
Carlos H Schenck ◽  
...  
Keyword(s):  
Rem Sleep ◽  
Serotonin Reuptake ◽  
Treatment Duration ◽  
Muscle Tone ◽  
Sleep Stage ◽  
Nrem Sleep ◽  
Sleep Stages ◽  
Pharmacological Properties ◽  
Age And Sex

Abstract Study Objectives To assess if selective serotonin reuptake inhibitor (SSRI) antidepressants are able to modify the chin EMG tone during sleep also in children. Methods Twenty-three children and adolescents (12 girls, mean age 14.1 years, SD 2.94) under therapy with antidepressant for their mood disorder were consecutively recruited and had a PSG recording. Twenty-one were taking were taking SSRI and treatment duration was 2-12 months. An age- and sex matched group of 33 control children (17 girls, mean age 14.2 years, SD 2.83) and 24 children with narcolepsy type 1 (12 girls, mean age 13.7 years, SD 2.80) were also included. The Atonia Index was then computed for each NREM sleep stage and for REM sleep, also all EMG activations were counted. Results Atonia Index in all sleep stages was found to be significantly reduced in children with narcolepsy followed by the group taking SSRI antidepressants and the number of EMG activations was also increased in both groups. Fluoxetine, in particular, was found to be significantly associated with reduced Atonia index during NREM sleep stages N1, N2, and N3, and with increased number of EMG activations/hour during sleep stage N3. Conclusions Similarly to adults, SSRI antidepressants are able to modify the chin EMG tone also in children during REM sleep, as well as during NREM sleep stages. Different pharmacological properties of the different SSRI might explain the differential effect on chin tone during sleep found in this study.

scholarly journals A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 347 ◽  
Author(s):  
Daoshuang Geng ◽  
Daoguo Yang ◽  
Miao Cai ◽  
Lixia Zheng
Keyword(s):  
Sleep Disorders ◽  
Sleep Stage ◽  
Brain Activity ◽  
Wavelet Packet ◽  
Permutation Entropy ◽  
Sleep Stages ◽  
Scattering Method ◽  
Microwave Scattering ◽  
Functional Sites ◽  
The Brain

The aim of this study was to develop an integrated system of non-contact sleep stage detection and sleep disorder treatment for health monitoring. Hence, a method of brain activity detection based on microwave scattering technology instead of scalp electroencephalogram was developed to evaluate the sleep stage. First, microwaves at a specific frequency were used to penetrate the functional sites of the brain in patients with sleep disorders to change the firing frequency of the activated areas of the brain and analyze and evaluate statistically the effects on sleep improvement. Then, a wavelet packet algorithm was used to decompose the microwave transmission signal, the refined composite multiscale sample entropy, the refined composite multiscale fluctuation-based dispersion entropy and multivariate multiscale weighted permutation entropy were obtained as features from the wavelet packet coefficient. Finally, the mutual information-principal component analysis feature selection method was used to optimize the feature set and random forest was used to classify and evaluate the sleep stage. The results show that after four times of microwave modulation treatment, sleep efficiency improved continuously, the overall maintenance was above 80%, and the insomnia rate was reduced gradually. The overall classification accuracy of the four sleep stages was 86.4%. The results indicate that the microwaves with a certain frequency can treat sleep disorders and detect abnormal brain activity. Therefore, the microwave scattering method is of great significance in the development of a new brain disease treatment, diagnosis and clinical application system.

scholarly journals Nighttime sleep disorders in patients with daytime sleepiness in Parkinson s disease

2018 ◽  
Vol 10 (4) ◽  
pp. 23-29 ◽  
Author(s):  
M. R. Nodel ◽  
K. V. Shevtsova ◽  
G. V. Kovrov ◽  
N. N. Yakhno
Keyword(s):  
Sleep Disorders ◽  
Rem Sleep ◽  
Daytime Sleepiness ◽  
Rating Scale ◽  
Sleep Latency ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Yahr Stage ◽  
Sleep Behavior ◽  
Nighttime Sleep

Daytime sleepiness is one of the clinically significant non-motor manifestations of Parkinson's disease (PD). One of its insufficiently studied aspects is a relationship between daytime sleepiness and nighttime sleep disorders.Objective:to clarify the clinical characteristics of PD in patients with different types of daytime sleepiness and to estimate of the ratio of daytime sleepiness to clinical and polysomnographic characteristics of nighttime sleep in patients with advanced stages of PD.Patients and methods. The investigation included 110 patients (56 men and 54 women) (mean age, 63.78+0.6 years) with PD (Hoehn and Yahr stage 2.6+0.2; disease duration, 6.3+3.2 years) without dementia. All the patients received therapy with levodopa at a mean daily dose of 667.8 mg; 98 of them had the drug in combination with dopamine receptor agonists at a stable dose. The unified PD rating scale, the PD sleep scale (PDSS), and the Epworth sleepiness scale (ESS) were applied. Nocturnal polysomnography (PSG) and the multiple sleep latency test (MSLT) were performed.Results and discussion. There was daytime sleepiness in 44% of the patients: permanent sleepiness in 15%, sudden daytime sleep attacks (along with low daytime sleepiness (ESS) in 14%, and permanent drowsiness concurrent with sleep attacks in 15%. The PSG findings showed a decrease in sleep efficiency, an increase in the duration of the first stage of sleep, a reduction in the duration of the second and third sleep stages, an extension of rapid eye movement (REM) sleep latency, and frequent awakenings (sleep fragmentation). PSG also demonstrated REM sleep behavior disorders (RBD) in half of the examinees.Patients with sleep attacks differed from those with permanent drowsiness without sleep attacks with more severe sleep disorders (PDSS) and shorter sleep latency (MSLT). Patients with the RBD phenomenon had shorter sleep latency (MTLS) than those without this parasomnia. Patients with moderate or severe sleepiness (ESS scores of >10) differed from those with milder drowsiness (ESS scores of 410) and a lower representation of the third sleep stage.Conclusion.There is evidence for the association of daytime sleepiness in PD with reduced efficiency, changes in the nighttime sleep pattern, and RBD.

scholarly journals SCALP HIGH FREQUENCY OSCILLATION RATE DEPENDS ON SLEEP STAGE AND DECREASES WITH TIME SPENT IN SLEEP

2021 ◽  
Author(s):  
Dorottya Cserpan ◽  
Richard Rosch ◽  
Santo Pietro Lo Biundo ◽  
Johannes Sarnthein ◽  
Georgia Ramantani
Keyword(s):  
High Frequency ◽  
Time Window ◽  
Sleep Stage ◽  
Nrem Sleep ◽  
Interval Length ◽  
High Frequency Oscillation ◽  
Sleep Cycle ◽  
Sleep Stages ◽  
Pediatric Epilepsy ◽  
Scalp Eeg

High frequency oscillations (HFO) in scalp EEG are a new and promising epilepsy biomarker. HFO analysis is typically restricted to random and relatively brief sleep segments. However, considerable fluctuations of HFO rates have been observed over the recording nights, particularly in relation to sleep stages and cycles. Here, we identify the timing within the sleep period and the minimal data interval length that allow for sensitive and reproducible detection of scalp HFO. We selected 16 seizure-free whole-night scalp EEG recordings of children and adolescents with focal lesional epilepsy (median age 7.6 y, range 2.2-17.4 y). We used an automated and clinically validated HFO detector to determine HFO rates (80-250 Hz) in bipolar channels. To identify significant variability over different NREM sleep stages and over time spent in sleep, we modelled HFO rate as a Poisson process. We analysed the test-retest reliability to evaluate the reproducibility of HFO detection across recording intervals. Scalp HFO rates were higher in N3 than in N2 sleep and highest in the first sleep cycle, decreasing with time spent in sleep. In N3 sleep, the median reliability of HFO detection increased from 67% to 79% to 100% for 5-, 10-, and 15-min data intervals, improving significantly (p=0.004) from 5 to 10 min but not from 10 to 15 min. In this analysis of whole-night scalp EEG, we identified the first N3 sleep stage as the most sensitive time window for HFO rate detection. N3 data intervals of 10 min duration are required and sufficient for reliable measurements of HFO rates. Our study provides a robust and reliable framework for implementing scalp HFO as an EEG biomarker in pediatric epilepsy.

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