Deep Sleep Stages Research Articles

Age-related network topological difference based on the sleep ECG signal

Objective: Age has been shown to be a crucial factor for the EEG and fMRI small-world networks during sleep. However, the characteristics of the age-related network based on the sleep ECG signal and how the network changes during different sleep stages are poorly understood. This study focuses on exploring the age-related scale-free and small-world network properties of the ECG signal from male subjects during distinct sleep stages, including the wakeful (W), light sleep (LS), deep sleep (DS) and rapid eye movement (REM) stages. Approach: The subjects are divided into two age groups: a younger (age ⩽ 40, n = 11) group and an older group (age > 40, n = 25). Main results: For the scale-free network analysis, our results reveal a distinctive pattern of the scale free network topologies between the two age groups, including the mean degree (), the clustering coefficient (), and the path length () features, such as the slope distribution of in the younger group increased from 1.99 during W to above 2.05 during DS. In addition, the results indicate that the small-world properties can be found across all sleep stages in both age groups. However, the small-world index in the LS and REM stages significantly decreased with age (p = 0.0006 and p = 0.05, respectively). Significance: The comparison analysis result indicates that the network topology variations in the sleep ECG signals are prone to show age-relevant differences that could be used for sleep stage classification and sleep disorder diagnosis.

In-Home Sleep Recordings in Military Veterans With Posttraumatic Stress Disorder Reveal Less REM and Deep Sleep

Veterans with posttraumatic stress disorder (PTSD) often report suboptimal sleep quality, often described as lack of restfulness for unknown reasons. These experiences are sometimes difficult to objectively quantify in sleep lab assessments. Here, we used a streamlined sleep assessment tool to record in-home 2-channel electroencephalogram (EEG) with concurrent collection of electrodermal activity (EDA) and acceleration. Data from a single forehead channel were transformed into a whole-night spectrogram, and sleep stages were classified using a fully automated algorithm. For this study, 71 control subjects and 60 military-related PTSD subjects were analyzed for percentage of time spent in Light, Hi Deep (1–3 Hz), Lo Deep (<1 Hz), and rapid eye movement (REM) sleep stages, as well as sleep efficiency and fragmentation. The results showed a significant tendency for PTSD sleepers to spend a smaller percentage of the night in REM (p < 0.0001) and Lo Deep (p = 0.001) sleep, while spending a larger percentage of the night in Hi Deep (p < 0.0001) sleep. The percentage of combined Hi+Lo Deep sleep did not differ between groups. All sleepers usually showed EDA peaks during Lo, but not Hi, Deep sleep; however, PTSD sleepers were more likely to lack EDA peaks altogether, which usually coincided with a lack of Lo Deep sleep. Linear regressions with all subjects showed that a decreased percentage of REM sleep in PTSD sleepers was accounted for by age, prazosin, SSRIs and SNRIs (p < 0.02), while decreased Lo Deep and increased Hi Deep in the PTSD group could not be accounted for by any factor in this study (p < 0.005). Linear regression models with only the PTSD group showed that decreased REM correlated with self-reported depression, as measured with the Depression, Anxiety, and Stress Scales (DASS; p < 0.00001). DASS anxiety was associated with increased REM time (p < 0.0001). This study shows altered sleep patterns in sleepers with PTSD that can be partially accounted for by age and medication use; however, differences in deep sleep related to PTSD could not be linked to any known factor. With several medications [prazosin, selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs); p < 0.03], as well as SSRIs were associated with less sleep efficiency (b = -3.3 ± 0.95; p = 0.0005) and more sleep fragmentation (b = -1.7 ± 0.51; p = 0.0009). Anti-psychotics were associated with less sleep efficiency (b = -4.9 ± 1.4; p = 0.0004). Sleep efficiency was negatively impacted by SSRIs, antipsychotic medications, and depression (p < 0.008). Increased sleep fragmentation was associated with SSRIs, SNRIs, and anxiety (p < 0.009), while prazosin and antipsychotic medications correlated with decreased sleep fragmentation (p < 0.05).

Effects of Aging on Cortical Neural Dynamics and Local Sleep Homeostasis in Mice.

Healthy aging is associated with marked effects on sleep, including its daily amount and architecture, as well as the specific EEG oscillations. Neither the neurophysiological underpinnings nor the biological significance of these changes are understood, and crucially the question remains whether aging is associated with reduced sleep need or a diminished capacity to generate sufficient sleep. Here we tested the hypothesis that aging may affect local cortical networks, disrupting the capacity to generate and sustain sleep oscillations, and with it the local homeostatic response to sleep loss. We performed chronic recordings of cortical neural activity and local field potentials from the motor cortex in young and older male C57BL/6J mice, during spontaneous waking and sleep, as well as during sleep after sleep deprivation. In older animals, we observed an increase in the incidence of non-rapid eye movement sleep local field potential slow waves and their associated neuronal silent (OFF) periods, whereas the overall pattern of state-dependent cortical neuronal firing was generally similar between ages. Furthermore, we observed that the response to sleep deprivation at the level of local cortical network activity was not affected by aging. Our data thus suggest that the local cortical neural dynamics and local sleep homeostatic mechanisms, at least in the motor cortex, are not impaired during healthy senescence in mice. This indicates that powerful protective or compensatory mechanisms may exist to maintain neuronal function stable across the life span, counteracting global changes in sleep amount and architecture.SIGNIFICANCE STATEMENT The biological significance of age-dependent changes in sleep is unknown but may reflect either a diminished sleep need or a reduced capacity to generate deep sleep stages. As aging has been linked to profound disruptions in cortical sleep oscillations and because sleep need is reflected in specific patterns of cortical activity, we performed chronic electrophysiological recordings of cortical neural activity during waking, sleep, and after sleep deprivation from young and older mice. We found that all main hallmarks of cortical activity during spontaneous sleep and recovery sleep after sleep deprivation were largely intact in older mice, suggesting that the well-described age-related changes in global sleep are unlikely to arise from a disruption of local network dynamics within the neocortex.

P01-Comparison of short-time Fourier transform and continuous wavelet transform for frequency analysis of sleep EEG

We aimed to comparison of the most common methods for time-frequency analysis of biological signals, short-time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). This comparison was performed over sleep EEG data. We have acquired frequency-to-time graphs, spectrograms for STFT and scalograms for CWT. It has been confirmed that the results of STFT are significantly affected by the size of the sliding window through which the STFT is performed. The rapid changes in the EEG like movement artifacts were more noticeable when the window size was around 1–3 s. Conversely, the slow changes associated with deep sleep or sleep stage transitions were most pronounced with a window size over 60 s. This drawback disappears using the CWT method. We use CWT with the analytic Morse wavelet, symmetry parameter of 3 and a time-bandwidth product of 60. The resulting scalogram shows both faster and slower changes in EEG. Especially in the low-frequency range; it is possible to distinguish different deep sleep patterns. The described approaches may facilitate the visual evaluation of long-term sleep EEG recordings, or allow effective analysis of an unknown EEG signal structure. The research has been supported by the project No. 17-20480 of the GA CR.

Impact of CPAP therapy on erectile function during sleep and testosterone level in men with sleep apnea syndrome

The article presents the results of a polysomnographic study with simultaneous monitoring spontaneous nocturnal erections - Nocturnal Penile Tumescences (NPT) in 69 men with age-related androgen deficiency (AAD). Of these, 45 patients were diagnosed with obstructive sleep apnea syndrome (OSAS). In 34 of the 45 patients with OSAS, regimens of non-invasive ventilation with positive-positive airway pressure (CPAP) were selected. Titration was carried out using automatic CPAP-devices for 2 months. The control group (n=24) comprised men with AAD, matched by the "copy-pair" type without clinical manifestations of respiratory disturbances during sleep. Extreme fragmentation of sleep with a significant predominance of the 1st and 2nd stages of the slow sleep phase, the deficiency of deep sleep stages and the significant shortening of the fast sleep phase was leveled by using a constant positive pressure in the upper respiratory tract. CPAP-therapy produced the phenomenon of a "rebound" of slow-wave sleep and fast-sleep phase. Before treatment, men with OSAS had a disruptive pattern of NPT (quantitative and qualitative characteristics). CPAP-therapy resulted in its recovery and maximum preservation relative to fast sleep phase time boundaries. However, the trend towards increasing testosterone levels was not significant, which requires additional testosterone replacement therapy concurrently with CPAP therapy in patients with OSAS and AAD.

Fractal scaling behavior of heart rate variability in response to meditation techniques

The rescaled range (R/S) analysis was used for analyzing the fractal scaling properties of heart rate variability (HRV) of subjects undergoing premeditation and meditation states. Eight novice subjects and four advanced practitioners were considered. The corresponding pre-meditation and meditation HRV data were obtained from the Physionet database. The results showed that mindfulness meditation induces a decrement of the HRV long-range scaling correlations as quantified with the time-variant Hurst exponent. The Hurst exponent for advanced meditation practitioners decreases up to values of 0.5, reflecting uncorrelated (e.g., white noise-like) HRV dynamics. Some parallelisms between mindfulness meditation and deep sleep (Stage 4) are discussed, suggesting that the former can be regarded as a type of induced deep sleep-like dynamics.

Low doses of mirtazapine or quetiapine for transient insomnia: A randomised, double-blind, cross-over, placebo-controlled trial.

Low doses of the antidepressant mirtazapine or the neuroleptic quetiapine are often prescribed off-label for insomnia. However, studies on the effects on sleep and hangover effects the following day are scarce. In this randomised, double-blind, cross-over, placebo-controlled trial, the influence of 7.5 mg mirtazapine and 50 mg quetiapine on both normal sleep and sleep disturbed by acoustic stress (traffic noise) as a model for transient insomnia was assessed. Additionally, hangover effects on next-day alertness and cognitive functioning were examined. A total of 19 healthy men without sleep complaints completed three treatment sessions, each session consisting of three consecutive nights in one of the mirtazapine, quetiapine or placebo conditions. Sleep was assessed using polysomnography and the Leeds Sleep Evaluation Questionnaire. Daytime sleepiness and cognitive functioning were assessed using the Leeds Sleep Evaluation Questionnaire, Karolinska Sleepiness Scale, Digit Symbol Substitution Task, Psychomotor Vigilance Task and an addition task. Under acoustic stress, both mirtazapine and quetiapine increased total sleep time by half an hour and reduced the number of awakenings by 35-40% compared to placebo. While quetiapine specifically increased the duration of non-rapid eye movement sleep, stage N2, mirtazapine mainly increased deep sleep stage N3. Subjects reported that both mirtazapine and quetiapine eased getting to sleep and improved sleep quality. Both drugs caused daytime sleepiness and lessened sustained attention. These findings support the use of low doses of mirtazapine and quetiapine for the treatment of insomnia. Further prospective studies on the long-term effects regarding effectiveness and adverse effects are needed.

Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity.

Brain activity during sleep is a powerful marker of overall health, but sleep lab testing is prohibitively expensive and only indicated for major sleep disorders. This report demonstrates that mobile 2-channel in-home electroencephalogram (EEG) recording devices provided sufficient information to detect and visualize sleep EEG. Displaying whole-night sleep EEG in a spectral display allowed for quick assessment of general sleep stability, cycle lengths, stage lengths, dominant frequencies and other indices of sleep quality. By visualizing spectral data down to 0.1 Hz, a differentiation emerged between slow-wave sleep with dominant frequency between 0.1–1 Hz or 1–3 Hz, but rarely both. Thus, we present here the new designations, Hi and Lo Deep sleep, according to the frequency range with dominant power. Simultaneously recorded electrodermal activity (EDA) was primarily associated with Lo Deep and very rarely with Hi Deep or any other stage. Therefore, Hi and Lo Deep sleep appear to be physiologically distinct states that may serve unique functions during sleep. We developed an algorithm to classify five stages (Awake, Light, Hi Deep, Lo Deep and rapid eye movement (REM)) using a Hidden Markov Model (HMM), model fitting with the expectation-maximization (EM) algorithm, and estimation of the most likely sleep state sequence by the Viterbi algorithm. The resulting automatically generated sleep hypnogram can help clinicians interpret the spectral display and help researchers computationally quantify sleep stages across participants. In conclusion, this study demonstrates the feasibility of in-home sleep EEG collection, a rapid and informative sleep report format, and novel deep sleep designations accounting for spectral and physiological differences.

Changes in cerebral hemodynamics during a sleep-deprived video-electroencephalogram in healthy children

This study investigates the cerebral hemodynamic changes during a routine sleep-deprived video-electroencephalogram (SD-VEEG) in healthy children. Forty-two children with normal intelligence were examined. The children were 5–14 years of age, and their electroencephalograms (EEGs) were within the normal range. Each subject was deprived of a routine night’s sleep and then examined during non-drug-induced sleep in the daytime. The awake and sleep stages were evaluated using EEGs, according to the American Academy of Sleep Medicine. Stable transcranial Doppler ultrasound (TCD) tracings through real-time TCD-VEEG monitoring were recorded. The mean systolic cerebral blood flow velocity (CBFV), diastolic CBFV, pulsatility index and resistance index of each artery were analyzed for 30 s per stage. A multivariate analysis of variance was conducted to compare the hemodynamic parameters for the awake stage versus light sleep and deep sleep stages. Non-rapid eye movement sleep was associated with an increased CBFV in the middle (164.38 ± 27.28) and anterior cerebral artery (131.81 ± 21.55) during light sleep (stages N1 and N2) (P = 0.0001), a reduced systolic CBFV in all vascular arteries (LMCA, 138.73 ± 20.64; LACA, 108.33 ± 22.33; LPCA, 83.9 ± 18.6) during deep sleep (stage N3) compared with light sleep (P = 0.0001), and a sustained increased PI (LMCA, 0.92 ± 0.13; LACA, 0.964 ± 0.18) during deep sleep (P < 0.05). These findings indicate distinct cerebral hemodynamic alterations during SD-VEEG in children. This study utilized real-time TCD-VEEG monitoring during SD-EEG to further investigate neurovascular coupling in interictal epileptic discharges and understand its potential influence on cognition in the developing brain.

Regional Patterns of Elevated Alpha and High-Frequency Electroencephalographic Activity during Nonrapid Eye Movement Sleep in Chronic Insomnia: A Pilot Study.

To examine nonrapid eye movement (NREM) sleep in insomnia using high-density electroencephalography (EEG). All-night sleep recordings with 256 channel high-density EEG were analyzed for 8 insomnia subjects (5 females) and 8 sex and age-matched controls without sleep complaints. Spectral analyses were conducted using unpaired t-tests and topographical differences between groups were assessed using statistical non-parametric mapping. Five minute segments of deep NREM sleep were further analyzed using sLORETA cortical source imaging. The initial topographic analysis of all-night NREM sleep EEG revealed that insomnia subjects had more high-frequency EEG activity (> 16 Hz) compared to good sleeping controls and that the difference between groups was widespread across the scalp. In addition, the analysis also showed that there was a more circumscribed difference in theta (4-8 Hz) and alpha (8-12 Hz) power bands between groups. When deep NREM sleep (N3) was examined separately, the high-frequency difference between groups diminished, whereas the higher regional alpha activity in insomnia subjects persisted. Source imaging analysis demonstrated that sensory and sensorimotor cortical areas consistently exhibited elevated levels of alpha activity during deep NREM sleep in insomnia subjects relative to good sleeping controls. These results suggest that even during the deepest stage of sleep, sensory and sensorimotor areas in insomnia subjects may still be relatively active compared to control subjects and to the rest of the sleeping brain.

Chemosensory stimulation during sleep – Arousal responses to gustatory stimulation

The processing of nociceptive, visual, vibrotactile, thermal and acoustic stimuli during sleep has been extensively investigated in the past. Recently, interest has focused on the impact of olfactory stimulation on sleep. In contrast to all other sensory systems, olfactory stimulation does not lead to an increased arousal frequency, regardless of hedonicity and concentration. The impact of the second chemosensory system, gustation, on sleep however has not been investigated to date.Twenty-one normosmic and normogeusic volunteers of both genders, aged 19–33years, participated in the trial. Stimulation was performed with a gustometer using the following aqueous solutions: saccharose 20% (sweet), sodium chloride (NaCl) 7.5% (salty), citrate 5% (sour), and quinine 0.02% (bitter). A tasteless solution was used as negative control. Capsaicin, a strong trigeminal stimulus, served as positive control. Primary outcome was arousal frequency per stimulus in each sleep stage, as assessed with polysomnography.The frequency of arousals decreased in deeper sleep stages (N1: 211 arousals of 333 stimuli=63%, N2: 676/2728=25%, N3: 43/1378=3%, REM: 57/1010=6%). Statistically significant differences in terms of arousal frequency were found in N2 between the negative control and NaCl 100μl (p<0.001), saccharose 100μl, citrate 50μl & 100μl, and quinine 100μl (p<0.05). Capsaicin led to complete awakenings in 94% of stimuli (30/32).These results demonstrate that gustatory stimulation during sleep induces arousals depending on stimulus intensity and sleep stage, which is different to olfactory stimulation and may be related to differences in central processing of the two chemosensory systems.

Treatment of Obstructive Sleep Apnea Syndrome through Orthodontics and Orthognathic Surgery: A Case Report with a Five Year Follow-Up

Introduction: Obstructive sleep apnea (OSA) results from the presence of abnormal soft and hard tissues in the upper airway, which cause the collapse of the oropharynx in the deeper stages of sleep. Different treatment modalities have been proposed for OSA, aiming to offer a better quality of life for the patients. Orthognathic surgery via maxillary-mandibular advancement (MMA) decreases the collapse of soft and hard tissue structures during sleep and has a high success rate. The facial and occlusal improvement, the enhancement on respiratory function and the less morbid postoperative period are some advantages responsible for the growing acceptance of this technique. Case report: This case reports a combination of orthodontic treatment and orthognathic surgery via MMA in a 51 year old man with OSA. The initial polysomnography showed an apnea-hypopnea index of 49.2 and arousal index of 21.7. Follow-up polysomnography performed 15 months after surgery revealed significant improvement. The apneahypopnea index decreased to 2.9 and the arousal index to 12.3. The patient reported significant improvements in life quality, sleep quality, and nasal breathing capacity, as well as a decrease in daytime sleepiness and snoring. Thus far, the patient has been under postoperative follow-up for 5 years and 6 months, without any complaints of breathing difficulties during the day or night. Conclusion: The combination of orthodontic and surgical treatment via MMA may be a favorable alternative treatment for improving the respiratory symptoms of patients with OSA and is highly indicated for patients with maxillary-mandibular discrepancies.

Impact of Ramadan intermittent fasting on cognitive function in trained cyclists: a pilot study.

This study assessed selected measures of cognitive function in trained cyclists who observed daylight fasting during Ramadan. Eleven cyclists volunteered to participate (age: 21.6±4.8 years, VO2max: 57.7±5.6 ml kg−1·min−1) and were followed for 2 months. Cognitive function (Cambridge Neuropsychological Test Automated Battery (CANTAB), Reaction Time index (RTI) and Rapid Visual Information Processing (RVP) tests) and sleep architecture (ambulatory EEG) were assessed: before Ramadan (BR), in the 1st week (RA1) and 4th week of Ramadan (RA4), and 2 weeks post-Ramadan (PR). Both cognitive tests were performed twice per day: before and after Ramadan at 8-10 a.m. and 4-6 p.m., and during Ramadan at 4-6 p.m. and 0-2 a.m., respectively. Training load (TL) by the rating of perceived exertion (RPE) method and wellness (Hooper index) were measured daily. If the TL increased over the study period, this variable was stable during Ramadan. The perceived fatigue and delayed onset muscle soreness (DOMS) increased at RA4. Sleep patterns and architecture showed clear disturbances, with significant increases in the number of awakenings and light sleep durations during Ramadan (RA1 and RA4), together with decreased durations of deep and REM sleep stages at PR. RTI (simple and multiple reaction index) reaction and movement times did not vary over the study period. The RVP test showed reduced false alarms during Ramadan, suggesting reduced impulsivity. Overall accuracy significantly increased at RA1, RA4 and PR compared to baseline. At RA4, the accuracy was higher at 0-2 a.m. compared to 4-6 p.m. Despite the observed disturbances in sleep architecture, Ramadan fasting did not negatively impact the cognitive performance of trained cyclists from the Middle East.

POLYSOMNOGRAPHY

Polysomnography (PSG) is the gold standard diagnostic test for several sleep disorders. It records , analyzes, &amp; interprets multiple simultaneous physiologic characteristics during sleep. These parameters include brain waves, the oxygen level in the blood, heart rate and breathing, body position, as well as eye and leg movements, along with synchronized audiovisual monitoring. Moreover, in certain conditions, additional parameters may be included such as esophageal pH monitoring, esophageal manometry , and overnight blood pressure monitoring. The test is usually performed at a sleep disorders unit within a hospital or at a sleep disorders centre. Sleep architecture is largely divided into non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. NREM is further divided into three stages: N1, N2, and N3 : N3 being the deepest stage of sleep. REM sleep alternates with NREM sleep and a normal person usually has 4–6 cycles of REM and NREM sleep . Monitoring of the different sleep stages, sleep interruptions, movements, and the other respiratory and cardiac signals are clinically helpful for identifying the nature of patient’s sleep problems and assessing response to treatment. JMS 2015;18(2):165-166

Heartbeat-related EEG amplitude and phase modulations from wakefulness to deep sleep: Interactions with sleep spindles and slow oscillations.

Based on physiological models of neurovisceral integration, different studies have shown how cognitive processes modulate heart rate and how the heartbeat, on the other hand, modulates brain activity. We tried to further determine interactions between cardiac and electrical brain activity by means of EEG. We investigated how the heartbeat modulates EEG in 23 healthy controls from wakefulness to deep sleep and showed that frontocentral heartbeat evoked EEG amplitude and phase locking (as measured by intertrial phase locking), at about 300-400 ms after the R peak, decreased with increasing sleep depth with a renewed increase during REM sleep, which underpins the assumption that the heartbeat evoked positivity constitutes an active frontocortical response to the heartbeat. Additionally, we found that individual heart rate was correlated with the frequency of the EEG's spectral peak (i.e., alpha peak frequency during wakefulness). This correlation was strongest during wakefulness and declined linearly with increasing sleep depth. Furthermore, we show that the QRS complex modulates spindle phase possibly related to the correspondence between the frequency of the QRS complex and the spindle frequency of about 12-15 Hz. Finally, during deep sleep stages, a loose temporal coupling between heartbeats and slow oscillation (0.8 Hz) could be observed. These findings indicate that cardiac activity such as heart rate or individual heartbeats can modulate or be modulated by ongoing oscillatory brain activity.

Sleep smart-optimizing sleep for declarative learning and memory.

The last decade has witnessed a spurt of new publications documenting sleep's essential contribution to the brains ability to form lasting memories. For the declarative memory domain, slow wave sleep (the deepest sleep stage) has the greatest beneficial effect on the consolidation of memories acquired during preceding wakefulness. The finding that newly encoded memories become reactivated during subsequent sleep fostered the idea that reactivation leads to the strengthening and transformation of the memory trace. According to the active system consolidation account, trace reactivation leads to the redistribution of the transient memory representations from the hippocampus to the long-lasting knowledge networks of the cortex. Apart from consolidating previously learned information, sleep also facilitates the encoding of new memories after sleep, which probably relies on the renormalization of synaptic weights during sleep as suggested by the synaptic homeostasis theory. During wakefulness overshooting potentiation causes an imbalance in synaptic weights that is countered by synaptic downscaling during subsequent sleep. This review briefly introduces the basic concepts and central findings of the research on sleep and memory, and discusses implications of this lab-based work for everyday applications to make the best possible use of sleep's beneficial effect on learning and memory.

Time delay between cardiac and brain activity during sleep transitions

Human sleep consists of wake, rapid-eye-movement (REM) sleep, and non-REM (NREM) sleep that includes light and deep sleep stages. This work investigated the time delay between changes of cardiac and brain activity for sleep transitions. Here, the brain activity was quantified by electroencephalographic (EEG) mean frequency and the cardiac parameters included heart rate, standard deviation of heartbeat intervals, and their low- and high-frequency spectral powers. Using a cross-correlation analysis, we found that the cardiac variations during wake-sleep and NREM sleep transitions preceded the EEG changes by 1–3 min but this was not the case for REM sleep transitions. These important findings can be further used to predict the onset and ending of some sleep stages in an early manner.

The relationship between the level of aggression and the sleep quality in patients with posttraumatic stress disorder

Subject: Sleep disorders are common problems in post-traumatic stress disorder (PTSD) patients. The main symptoms associated with sleep disorders were reported as a reduction in total sleep time, prolonged sleep latency, increase in the number wakefulness, low sleep efficiency and a reduction in the percentage of deep sleep stages. A relation between sleep disorders and increased levels of aggression was determined. In this study, we aimed to examine the relationship between the quality of sleep and the level of aggression in patients with Post Traumatic Stress Disorder. Method: The study was included 48 patients with a diagnosis of PTSD who were hospitalized. Data were collected using post-traumatic stress disorder questionnaire scale, Pittsburgh sleep quality scale and the Buss-Perry Aggression Scale applied by the clinician. Data analysis was made using SPSS 15.0 package program. Descriptive statistics and Pearson’s correlation test were used in statistical analysis. Results: The average age of the cases was 32.33 ± 7.93, the average of the symptoms of post-traumatic stress disorder was 56.23 ± 11.14, the average of sleep quality was 22.08 ± 6.67 and the average of aggression levels was 51.67 ± 7.48. There was a high level deterioration in sleep quality and aggression levels were medium/medium-high levels. It was determined that Buss-Perry Aggression Scale scores were positively correlated with Pittsburgh sleep quality and posttraumatic stress disorder total and hyper arousal subscale scores. Conclusion: In this study, it is stated that increase in overstimulation symptoms and degradation of sleep in PTSD patients cause increase in agression levels. Degradation of sleep is determined as an indicator of agression that is observed in TSSB patients. Associatively, in PTSD patients, use of interventions to improve the quality of sleep oriented agressive behaviours gain value

Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal.

The existing sleep stages classification methods are mainly based on time or frequency features. This paper classifies the sleep stages based on graph domain features from a single-channel electroencephalogram (EEG) signal. First, each epoch (30 s) EEG signal is mapped into a visibility graph (VG) and a horizontal VG (HVG). Second, a difference VG (DVG) is obtained by subtracting the edges set of the HVG from the edges set of the VG to extract essential degree sequences and to detect the gait-related movement artifact recordings. The mean degrees (MDs) and degree distributions (DDs) P (k) on HVGs and DVGs are analyzed epoch-by-epoch from 14,963 segments of EEG signals. Then, the MDs of each DVG and HVG and seven distinguishable DD values of P (k) from each DVG are extracted. Finally, nine extracted features are forwarded to a support vector machine to classify the sleep stages into two, three, four, five, and six states. The accuracy and kappa coefficients of six-state classification are 87.5% and 0.81, respectively. It was found that the MDs of the VGs on the deep sleep stage are higher than those on the awake and light sleep stages, and the MDs of the HVGs are just the reverse.

The Dangers of Sleep Deprivation

T he D angers of S leep D eprivation Nithya Lingampalli B S J During President Ronald Reagan’s term as president, he went on a state visit to Paraguay. As cameras were documenting the occasion and broadcasting it for the world to see, a surprising shot was included. When the cameras focused on Reagan during the Paraguayan president’s speech, he was asleep! His wife quickly woke him up, and then he ascended the stage to deliver his address. However, when he started speaking, he said that he was very happy to be in Uruguay while everyone else smiled politely (Schneider, 2014). This instance illustrates a national health problem, often unrecognized as a severe issue, that many suffer from on a daily basis. Sleep deprivation, either a chronic or transient state, is when the lack of the requisite amount of sleep for upkeep causes the body to experience many physical and mental problems. According to data from the National Health Interview Survey, “nearly 30% of adults reported an average of ≤6 hours of sleep per day in 2005-2007. In 2009, only 31% of high school students reported getting at least 8 hours of sleep on an average school night” (Insufficient Sleep Is a Public Health Epidemic, 2014). Children appear to be most affected by sleep deprivation as a recent study from Boston College suggests that, “70 to 80 percent of school-aged children fall short of experts’ recommendations,” a higher percentage than any other country in the world (Liese, 2014). “According to data from the National Health Interview Survey, “nearly 30% of adults reported an average of ≤6 hours of sleep per day in 2005-2007. In 2009, only 31% of high school students reported getting at least 8 hours of sleep on an average school night” To put the severity of the problem into perspective, the recommended amount of sleep, as suggested by The National Institute of Health, is at least 10 hours for school-age children, 9-10 hours for teens, and 7-8 hours for adults (Insufficient Sleep Is a Public Health Epidemic, 2014). Although there is a variation from these standard times between individuals due to different body requirements, these represent the average amount of time necessary based upon a large sample of the population, and thus serve as a baseline to evaluate individual situations. We see that regardless of the age group, people are falling short of their recommended sleep quota by two hours or more, often on a daily basis. The biological mechanism of sleep deprivation deals with the reduced amount of REM and deep sleep stages, hallmarks of healthy sleep. Normal sleep is divided into “Some of the most interesting [causes] are broad scale societal factors such as the pervasive presence of technology and extremely busy schedules.” two stages, non-rapid eye movement (NREM) and rapid eye movement (REM). NREM sleep is further divided into three sub stages (N1, N2, and N3) that progress sequentially and represent an increasing degree of deep sleep. Stage N3, also known as delta-wave sleep, is the deepest stage of sleep and requires a very strong stimulus to be broken Although many areas of the brain are involved in sleep regulation, the hypothalamus seems to be the key component (Stevens, 2013). There are many causes, both direct and indirect, that contribute to sleep deprivation. Some of the most interesting are broad scale societal factors such as the pervasive presence of technology and extremely busy schedules. Access to technology has never been as ubiquitous as it is today, and its influence is growing even more in our daily lives. Phones, tablets, laptops, television, MP3 players, and many more devices are always within reach, at any time of the day. They are alluring due to the vast amount of information they offer a portal to. As a result, many people find it increasingly difficult to separate themselves from their devices, and often use them right until the moment they sleep, or sometimes fall asleep using them. However, using technological devices right before bed, or even some time before, has been shown to negatively impact the ease and quality of sleep. The light from the devices suppresses the levels of melatonin, a hormone that signals to the brain to 21 • B erkeley S cientific J ournal • E xtremes • F all 2014 • V olume 19 • I ssue 1