Sleep State Research Articles

Drawing promotes memory retention in a patient with sleep-related anterograde amnesia.

Drawing is a powerful tool to enhance memory in healthy participants and patients with probable dementia. Here, we investigated whether the drawing effect could extend to patient CT, a young woman with severe anterograde amnesia. Following surgery for a midline tumor involving her septum pellucidium and extending down into her fornices bilaterally, CT experienced a severe case of sleep-related amnesia. She can remember information encountered throughout the day, but when waking up in the morning or following a nap she forgets information learned prior to sleep. Here, we tested CT and 21 age-matched controls in a 3-day within-subjects design, during which participants encoded words by either drawing or writing them down. Memory for encoded words was tested in two conditions that each followed a 12-h delay, once after a night of sleep, and once after 12 h of wake. Despite her severe memory impairment, CT showed a drawing effect that was comparable to controls in both sleep and wake conditions. Whereas CT's memory for written words was consistently impaired relative to controls, her memory for drawn words was at the lower control range following a waking delay and above chance following a sleep delay. We suggest that amnesic patients may benefit from the drawing effect due to the recruitment of brain regions outside of the hippocampal system for encoding and consolidation. Furthermore, in control participants, sleep benefited memory for written words, but not for drawn words, suggesting that sleep preferentially consolidates memories that are more dependent on the hippocampal system.

A Novel Method for Optimising Energy Efficiency in High-Performance Computing Systems

This paper introduces a new method to promote energy efficiency in computing systems. The rapidly growing demand for computational power in high-performance computing (HPC) systems is accompanied by a significant increase in energy consumption. This research investigates the many ways to minimise energy consumption in HPC systems without sacrificing computational performance. Building upon previous research we combined and refined ideas to develop an optimised approach. Our method utilises a tri-modular framework incorporating an Energy-Efficient Hardware Design, Resource Management, and Optimisation, and Server Virtualisation. The first module employs a method of designing smart energy-efficient hardware. This method incorporates leakage reduction techniques such as clock gating and sleep states. The second module uses a technique focused on optimising server software. This method is based on Dynamic Voltage and frequency scaling (DVFS) for power management in data centres. The third module is based on server virtualisation and employs software to create multiple virtual machines on a single physical server allowing for a significant reduction in energy and hardware costs. The methods used in these three modules are systematically integrated to produce a more efficient consumption of electricity. This will allow for the computing system to minimise energy consumption without compromising computational power.

Cognitive impairment and depression precede increased HDL-C levels in middle-aged and older Chinese adults: cross-lagged panel analyses

BackgroundHigh-density lipoprotein cholesterol (HDL-C) is widely recognized for its protective effects against cognitive decline. However, recent studies have presented conflicting results, with some suggesting no significant cognitive benefits or even an increased risk of dementia associated with high HDL-C levels. For those who suffer from depression, the cognitive benefits of HDL-C may be diminished or reversed. The purpose of this study is to investigate the associations between HDL-C, cognitive ability, and depressive symptoms in middle-aged and older Chinese adults.MethodsThe datasets utilized were sourced from the China Health and Retirement Longitudinal Study (CHARLS) for the years 2011 and 2015, comprising 4,302 participants. Cross-lagged models were employed to explore the temporal sequence between cognitive performance and HDL-C levels, and to examine the interplay among depression, cognition, and HDL-C. Confounding factors such as sociodemographic characteristics, sleep conditions, and history of chronic diseases were controlled for.ResultsThe analysis revealed unidirectional effects of baseline impaired cognition and greater severity of depression on increased HDL-C levels at follow-up (β = − 0.036 and β = 0.028, respectively, P < 0.05). However, higher baseline HDL-C levels did not significantly predict cognitive performance or depression 4 years later (β = − 0.008 and β = 0.023, respectively, P > 0.05). Depressive symptoms and cognition were found to have a significant bidirectional association (β = − 0.026 and β = − 0.053, respectively, P < 0.05).ConclusionsCognitive impairment and depression are associated with higher HDL-C levels, whereas higher HDL-C levels do not appear to protect against cognitive decline or depressive symptoms. These findings underscore the importance of preserving cognitive and mental health, which may lower the likelihood of cardiovascular disease and dementia. Future studies should validate these findings and develop targeted interventions tailored to specific populations.

Electroencephalogram and phenotype patterns in neuronopathic Gaucher disease patients – ten years of experience in a single center

BackgroundThis study aimed to investigate the unique electroencephalography (EEG) patterns in neuronopathic Gaucher disease (GD) patients and explore the correlations between EEG findings and neurological phenotypes so as to optimize clinical outcomes.MethodsA retrospective analysis was conducted on 74 EEG recordings from 50 GD patients between January 2012 and July 2022.ResultsTwenty-three patients exhibited abnormal EEG recordings, including 11 of the GD1 type (the transitional type) and 12 with neuronopathic GD. Of the 12 neuronopathic GD patients, 9 patients with epilepsy were analysed specifically in terms of the clinical course. The primary waveform observed in the neuronopathic EEG recordings was the spike-and-wave complex (SWC) during both awake and sleep states. This was significantly different from sharp waves observed only during sleep in the patients of the transitional type (P = 0.0230). The abnormal discharges in the neuronopathic patients were most commonly located in the bilateral Rolandic areas, while the transitional type commonly involved the bilateral frontal regions. Three patients with an epileptic EEG pattern reported their initial seizures years later. Seizures in the neuronopathic patients were effectively controlled with anti-seizure medications (ASMs), despite the ongoing presence of abnormal EEG patterns. The EEG patterns during ocular symptoms were characterized by sporadic or continuous unilateral SWC during sleep.ConclusionsPatients with neuronopathic GD exhibit distinct EEG patterns that can help differentiate them from GD1 patients. Early treatment with ASMs can effectively control seizures. EEG plays a crucial role in monitoring seizures and can facilitate prompt intervention for GD patients.

Improvements in daytime sleepiness and disrupted nighttime sleep with once-nightly sodium oxybate in people with narcolepsy type 1 and type 2: a plain language summary.

This is a plain language summary of a published article in the journal Sleep. Narcolepsy is a sleep condition that has 2 different subtypes: narcolepsy type 1 and narcolepsy type 2. These are called NT1 and NT2 for short. Sodium oxybate (SXB) is approved to treat excessive daytime sleepiness (EDS) and cataplexy. People with NT1 and NT2 both have EDS, but cataplexy is only present in people with NT1. Limited information is available about how SXB works in people with NT2. This is because previous trials have included only people with NT1 or people with unspecified narcolepsy. For more than 20years, the only available formulation of this medicine had to be given twice during the night. Many people with narcolepsy find that chronically waking up in the middle of the night for a second dose of SXB is disruptive to themselves or others in their household. People have also reported sleeping through alarm clocks, missing their second dose, and feeling worse the next day. Some people have accidentally taken the second dose too early, putting them at risk for serious adverse effects. These adverse effects may include slow breathing, low blood pressure, or sedation. The US Food and Drug Administration (FDA) approved a medicine called LUMRYZ™ (sodium oxybate) for extended-release oral suspension in May 2023. LUMRYZ is a once-nightly formulation of SXB (ON-SXB for short) and is taken as a single dose before bedtime. This medicine treats EDS and muscle weakness (also known as cataplexy) in people with narcolepsy. A clinical trial called REST-ON studied ON-SXB to find out if it was better at treating narcolepsy symptoms than a medicine with no active ingredients (placebo). This summary describes a study that tested whether ON-SXB was better than placebo at treating narcolepsy symptoms in people with NT1 or NT2. This study showed that compared to people who took placebo, people who took ON-SXB were able to stay awake longer during the day, felt less sleepy during the daytime, had less cataplexy, and had more improvements in their symptoms overall than people who took placebo. ON-SXB has been proven effective for people with NT1 or NT2. Unlike prior formulations of SXB, ON-SXB is taken once at bedtime, without requiring waking up in the middle of the night for a second dose.

Relationship Between Patient-reported Outcome Measures and Accelerometer-measured Physical Activity and Sleep in Patients With Postherpetic Neuralgia.

Available treatment for chronic neuropathic pain is still limited, and the positive effects are modest. Thus, clinicians aim to improve activity and quality of life despite pain. The objective monitoring of activity is attracting attention in chronic pain assessments. Therefore, we objectively evaluated daytime activity and sleep in patients with postherpetic neuralgia (PHN), using actigraphy to determine risk factors for decreased activity. Participants with PHN wore an actigraph (a wristwatch-like accelerometer) on the nondominant hand. The actigraph measured day-time activity and sleep, which were compared with participant-reported subjective pain and sleep assessments. Fifty-four individuals with PHN who visited our outpatient clinic completed questionnaires and a week of actigraph monitoring. Subjective scores of pain intensity, neuropathic pain, disability in daily life, pain-catastrophizing thoughts, and insomnia were all well correlated. However, the actigraph-monitored activity levels, using 2 equations, and sleep quality were not associated with any pain or sleep-related subjective scores. The discrepancy between the subjective and objective scores in this study may be due to (1) features of PHN, an archetype of peripheral neuropathic pain affecting no motor nerves, (2) actigraph measurement limitations regarding the sedentary life of the elderly, or (3) activity misperception, a new proposition explaining the discrepancy between subjective and objective measures of activity, similar to the sleep state misperception. In patients with PHN, high pain intensity may be reported in those with highly maintained activity, in which treatment must be selected cautiously to prevent interruption of their physical abilities.

How to quantify sleepiness during an attempt to sleep?

Background. Sleepiness assessment tools were mostly developed for detection of an elevated sleepiness level in the condition of sleep deprivation and several medical conditions. However, sleepiness occurs in various other conditions including the transition from wakefulness to sleep during an everyday attempt to get sleep.Objective. We examined whether objective sleepiness indexes can be implicated in detection of fluctuations in sleepiness level during the polysomnographically-monitored attempt to sleep, i.e. in the absence of self-reports on perceived sleepiness level throughout such an attempt.Approach. The polysomnographic signals were recorded in the afternoon throughout 106 90 min napping attempts of 53 university students (28 females). To calculate two objective sleepiness indexes, the electroencephalographic (EEG) spectra were averaged on 30 s epochs of each record, assigned to one of 5 sleep-wake stages, and scored using either the frequency weighting curve for sleepiness substate of wake state or loadings of each frequency on the 2nd principal component of variation in the EEG spectrum (either sleepiness score or PC2 score, respectively).Main results. We showed that statistically significant fluctuations in these two objective sleepiness indexes during epochs assigned to wake stage can be described in terms of the changes in verbally anchored levels of subjective sleepiness assessed by scoring on the 9-step Karolinska Sleepiness Scale.Significance. The results afford new opportunities to elaborate importance of intermediate substates between wake and sleep states for sleep-wake dynamics in healthy individuals and patients with disturbed sleep.

Application of a specialist nurse–led wireless smart monitoring system in elderly patients with persistent atrial fibrillation

Background: With the increase in the elderly population, the prevalence of persistent atrial fibrillation (AF) among older adults has significantly risen, becoming one of the crucial cardiovascular diseases affecting the health and quality of life of the elderly. Patients with persistent AF require continuous cardiac function monitoring to prevent possible complications such as stroke and heart failure. Traditional monitoring methods often necessitate frequent hospital visits, increasing the economic burden and psychological stress on patients. With advancements in medical technology, wireless smart monitoring systems have emerged as a new remote health care technology, offering a more convenient and effective monitoring option for patients with persistent AF. Specialist nurses play a pivotal role in monitoring and managing patients using these systems. Their professional knowledge and skills are vital for improving monitoring efficiency and patient compliance. Therefore, this study aims to explore the application of wireless smart monitoring systems led by specialist nurses in elderly patients with persistent AF, in hopes of providing a scientific basis for improving the monitoring and management of this patient group. Methods: A total of 88 elderly patients with persistent atrial fibrillation admitted to the Cardiology Ward of a tertiary hospital in Weifang City from October 2021 to April 2023 were selected as research subjects. They were divided into an intervention group and a control group with conventional care, with 44 cases in each group, according to the order of admission. The control group received conventional hospitalization guidance, while the intervention group utilized a wireless smart monitoring system led by specialist nurses. The effects before and after the intervention in both groups were compared. Results: The intervention group patients had better scores in terms of mental stress, sleep quality, and comfort compared with the control group, and the nursing shift change time during hospitalization for the intervention group was shorter than that of the control group (P&lt;0.05). Conclusions: The wireless smart monitoring system led by specialist nurses can improve the mental stress and sleep conditions of elderly patients with persistent atrial fibrillation, increase comfort, and enhance the efficiency of nursing shift changes.

Cognitive performance during exposure to moderate normobaric hypoxia after sleep restriction: Relationship to physiological and stress biomarkers

IntroductionExposure to moderate levels of simulated hypoxia has subtle cognitive effects relative to ground level, in healthy individuals. However, there are few data on the cognitive consequences of the combination of hypoxia and partial sleep deprivation, which is a classic military or civilian operational context. In this study, we tested the hypothesis that exposure to moderate hypoxia while sleep-restricted impairs several domains of cognition, and we also assessed physiological parameters and salivary concentrations of cortisol and alpha-amylase. MethodSeventeen healthy males completed two sessions of cognitive tests (sustained attention using the PVT psychomotor vigilance task and executive functions using the Go-NoGo inhibition task and N-Back working memory task) after 30 min (T + 30′) and 4 h (T + 240′) of exposure in a normobaric hypoxic tent (FIO2 = 13.6 %, ≃ 3,500 m) (HY). This was completed after one night of sleep restriction (3 a.m. to 6 a.m. bedtime, SRHY) and one night of habitual sleep (10 p.m. to 6 a.m. bedtime, HSHY) (with cross-over randomization). The two nights sleep architecture and physiological parameters (oxygen saturation (SpO2) and heart rate (HR) during T + 30′ and T + 240′sessions were analyzed. Salivary cortisol and alpha-amylase (sAA) concentrations were analyzed before hypoxia, after the T + 30′ and T + 240′ cognitive sessions, and after leaving the hypoxic tent. ResultsSustained attention (RT and number of lapses in the PVT) and executive functions (Go-NoGo and 1-Back and 2-Back parameters, as inhibition and working memory signatures) were impaired in the SRHY condition compared to HSHY. SpO2 and HR were higher after 4 h compared with 30 min of hypoxia in the HSHY condition, while only HR was statistically higher in the SRHY condition. In SRHY, salivary AA concentration was lower and cortisol was higher than in HSHY. A significant increase in sAA concentration is observed after the cognitive session at 4 h of hypoxia exposure compared to that at 30 min, only in the SRHY condition. There are significant positive correlations between reaction time and the corresponding heart rate (a non-invasive marker of physiological stress) for the executive tasks in the two sleep conditions. This was not observed for salivary levels of sAA and cortisol, respective reliable indicators of the sympathoadrenomedullary system and the hypothalamic-pituitary adrenocortical system. ConclusionExposure to moderate normobaric hypoxia (≃ 3500 m / ≃ 11,500 ft simulated) after a single night of 3-hour sleep impairs cognitive performance after 30 min and 4 h of exposure. The key determinants and/or mechanism(s) responsible for cognitive impairment when exposed to moderate hypoxia with sleep restriction, particularly on the executive function, have yet to be elucidated.

"I know when to stop": Sentinels and counter-strategies against drowsy driving in narcolepsy and idiopathic hypersomnia.

To collect prodromal symptoms experienced by participants with narcolepsy and idiopathic hypersomnia (considered "hypersomnolence experts") prior to drowsy driving and counter-strategies used to maintain alertness. Systematic, face-to-face interview (using a semi-structured questionnaire), including clinical measures, frequency of car accidents/near misses, and symptoms experienced before impending drowsy driving episodes and counter-strategies. Among 61 participants (32 with narcolepsy, 29 with idiopathic hypersomnia; 56 drivers), 61% of drivers had at least one lifetime accident/near miss. They had a higher sleepiness score (14 ± 4 vs. 11 ± 5, P<0.04) than those without an accident/near miss, but no other differences in demographics, driving experience, medical conditions, symptoms, sleep tests, and treatment. All but three participants experienced prodromal symptoms of drowsy driving, which included postural and motor changes (86.9%: axial hypotonia - e.g., eyelid droop, stereotyped movements), cognitive impairment (53.3%: automatic steering, difficulty concentrating/shifting, dissociation, mind wandering, dreaming), sensory (65%: paresthesia, pain, stiffness, heaviness, blunted perceptions such as a flat dashboard with loss of 3D, illusions and hallucinations), and autonomic symptoms (10%, altered heart/breath rate, penile erection). Counterstrategies included self-stimulation from external sources (pain, cold air, music, drinks, driving with bare feet), motor changes (upright posture, movements), and surprise (sudden braking). Drowsy driving symptoms can result from "local" NREM, entry in N1 sleep, and hybrid wake/REM sleep states. These rich qualitative insights from participants with narcolepsy and idiopathic hypersomnia, as well as sophisticated counter-strategies, can be gathered to reduce the crash risk in this population, but also in inexperienced healthy drivers.

Sleep conditions and sleep hygiene behaviors in early pregnancy are associated with gestational diabetes mellitus: A propensity-score matched study.

To explore the influence of sleep conditions and sleep hygiene behaviors in early pregnancy on gestational diabetes mellitus (GDM) development. This 1:1 propensity-score matched study included 1,216 pregnant women divided into GDM and control groups based on diagnosis via the oral glucose tolerance test at 24-28 gestational weeks. Sleep conditions and hygiene behaviors were evaluated using structural questionnaires, including the Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, and Sleep Hygiene Practice Scale. Univariate and multivariate logistic regression analyses and Spearman's correlation were conducted to identify the associations. After adjusting for baseline clinical characteristics, women with GDM were more likely to have poor sleep quality (adjusted odds ratio [AOR] = 1.585, 95% confidence interval [CI]: 1.261-1.992) and higher scores for subjective sleep quality, latency, duration, efficiency, and sleep disturbances (all P < 0.01). Mild sleepiness (AOR = 1.311, 95% CI: 1.012-1.699) and worrying about not being able to fall asleep (AOR = 1.123, 95% CI: 1.005-1.255) were more likely to occur in the GDM group. Sleep quality and hygiene behaviors such as sleep-irrelevant activities, staying in bed after waking up, weekend catch-up sleep, and overeating before bedtime were significantly correlated with gestational diabetes variables. Poor sleep conditions and specific sleep hygiene behaviors in early pregnancy may be independent risk factors for GDM. This suggests that sleep assessment and behavior education can be used as new approaches for the early implementation of surveillance and prevention of GDM.

Sleep misperception in women with myofascial temporomandibular disorder.

Temporomandibular disorders (TMD) were linked to poor sleep on the Pittsburgh Sleep Quality Index (PSQI), whereas polysomnography (PSG) revealed no major sleep disturbances, implying sleep state misperception (SSM). This study investigates SSM in TMD and control participants; correlates SSM with objective short sleep duration (SSD), depression symptoms, daytime sleepiness, and orofacial pain; and compares objective SSD between the groups. General linear models were used to compare second-night PSG total sleep time (TST), sleep latency (SL), sleep efficiency (SE) and wake after sleep onset (WASO) with homologous PSQI-derived variables in 124 women with myofascial TMD and 46 age and BMI matched controls. PSQI variables were regressed onto objective SSD, depression symptoms, daytime sleepiness, and pain. Lastly, objective SSD was related to TMD presence. Compared to controls, TMD cases misperceived SE (p = 0.02); depression symptoms explained PSQI-derived SE (p = 0.002) and mediated the effect of pain (p <.001). PSQI variables were unrelated to respective PSG measures or objective SSD, except a significant subjective-objective correlation in SE among controls only (p = 0.002). Objective SSD was more frequent in TMD cases (p = 0.02, OR = 2.95), but it was unrelated to depression symptoms, daytime sleepiness or pre-PSG pain. The study demonstrates misperception of SE among TMD cases, which was accounted for by depression symptoms. Objective SSD nearly tripled in TMD cases; however, it was unrelated to PSQI variables, depression, daytime sleepiness, or pain, suggesting that SSM and objective SSD are two independent sleep features in TMD.

WISNet: A deep neural network based human activity recognition system

Nowadays, Human Activity Recognition (HAR) is a key research area with many ubiquitous innovative solutions, where both accelerometer and gyroscope data provide information about an observed person’s physical activity. HAR offers a diverse variety of important applications, including healthcare, burglary detection, workplace monitoring, and emergency identification. Traditional recognition approaches rely on extracting handmade features from the obtained data to identify the typeof human action. Additionally, the efficacy of these works is dependent upon the specific customized features that are chosen. One potential approach to tackle this issue is to utilize Convolutional Neural Networks (CNN) to automatically learn the relevant features. In this paper, we propose a deep learning model, WISNet, a custom 1D-CNN approach to recognize six complex human activities: Jogging, Walking Downstairs, Sitting, Standing, Walking and Climbing Upstairs. The model includes a Convolved Normalized Pooled (CNPM) Block to generate significant features from the initial layers. An Identity and Basic (IDBN) Block is incorporated to extract residual progressive features for capturing complex sequential data dependencies. Channel and Spatial attention (CASb) Block is integrated with the network to prioritize or minimize essential features based on relative weights. The proposed WISNet model achieved an enhanced accuracy and F1-score of 96.41 % and 0.95 for the HAR dataset by surpassing the existing transfer learning architectures such as Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Recurrent Neural Network (SimpleRNN). By strategically integrating the CNPM, IDBN, and CASb blocks, this study aims to tackle distinct challenges encountered in the classification process by enhancing the discernment of features essential for the precise identification of multi-class human activity recognition. The seamless integration of these blocks within the model plays a pivotal role in elevating the overall performance of the WISNet architecture. The work also validates WISNet with similar open-source datasets (UCI-HAR and KU-HAR) and dissimilar open-source datasets (Sleep state detection, Fall detection, and ECG Heartbeat).

Autonomic cardiovascular control is unaffected in children referred for assessment of excessive daytime sleepiness.

There is conflicting evidence for impaired autonomic control of heart rate (HR) in adults with narcolepsy and idiopathic hypersomnolence (IH). Despite these chronic hypersomnia conditions primarily being diagnosed around the age of puberty, there are limited studies in children. The present study investigated cardiovascular control using heart rate variability (HRV) and the extent of nocturnal HR dipping during sleep in children and adolescents with narcolepsy and IH. Children having an overnight polysomnographic study followed by a multiple sleep latency test (MSLT) for investigation of excessive daytime sleepiness (EDS) between May 2010 to December 2023 were included: 28 children diagnosed with narcolepsy, 11 with IH, and 26 subjectively sleepy children who did not meet the diagnostic criteria for either narcolepsy or IH. Each clinically referred child was matched for age and sex with a control. Time domain and frequency domain HRV were calculated from ECG recorded at 512 Hz. There were no differences in either time domain or spectral analysis of HRV between clinical groups or between clinical groups and their control group. The expected sleep state differences in HRV were observed in all groups. There was also no difference in HR nocturnal dipping between groups. Despite evidence for abnormal autonomic function in adults with narcolepsy and IH, our study did not identify any abnormalities in HR, HR control, or nocturnal dipping of HR in children referred for assessment of EDS. This suggests that autonomic dysfunction may be a feature of these conditions that develops in later life.

Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data

BackgroundWide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. New methodA hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep. ResultsSleep states were classified with an accuracy of 84 % and Cohen’s κ of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. Comparison with existing methodOn a held out, repeated 3-hour WFCI recording, the CNN-BiLSTM achieved a κ of 0.67, comparable to a κ of 0.65 corresponding to the human EEG/EMG-based scoring. ConclusionsThe CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep research.

Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis.

Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems.

Energy-Efficient Clustering in Wireless Sensor Networks Using Grey Wolf Optimization and Enhanced CSMA/CA.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

The effects of shared, depression-specific, and anxiety-specific internalizing symptoms on negative and neutral episodic memories following post-learning sleep.

Emotional memory bias is a common characteristic of internalizing symptomatology and is enhanced during sleep. The current study employs bifactor S-1 modeling to disentangle depression-specific anhedonia, anxiety-specific anxious arousal, and the common internalizing factor, general distress, and test whether these internalizing symptoms interact with sleep to influence memory for emotional and neutral information. Healthy adults (N = 281) encoded scenes featuring either negative objects (e.g., a vicious looking snake) or neutral objects (e.g., a chipmunk) placed on neutral backgrounds (e.g., an outdoor scene). After a 12-hour period of daytime wakefulness (n = 140) or nocturnal sleep (n = 141), participants judged whether objects and backgrounds were the same, similar, or new compared with what they viewed during encoding. Participants also completed the mini version of the Mood and Anxiety Symptom Questionnaire. Higher anxious arousal predicted worse memory across all stimuli features, but only after a day spent being awake-not following a night of sleep. No significant effects were found for general distress and anhedonia in either the sleep or wake condition. In this study, internalizing symptoms were not associated with enhanced emotional memory. Instead, memory performance specifically in individuals with higher anxious arousal was impaired overall, regardless of emotional valence, but this was only the case when the retention interval spanned wakefulness (i.e., not when it spanned sleep). This suggests that sleep may confer a protective effect on general memory impairments associated with anxiety.

Sleep defined by arousal threshold reveals decreases in corticocortical functional correlations independently from the conventional sleep stages.

Sleep research and sleep medicine have benefited from the use of polysomnography but have also suffered from an overreliance on the conventional, polysomnography-defined sleep stages. For example, reports of sleep-specific brain activity patterns have, with few exceptions, been constrained by assessing brain function as it relates to the conventional sleep stages. This limits the variety of sleep states and underlying activity patterns that one can discover. If undiscovered brain activity patterns exist during sleep, then removing the constraint of a stage-specific analysis may uncover them. The current study used all-night functional magnetic resonance imaging sleep data and defined sleep behaviorally with auditory arousal threshold (AAT) to begin to search for new brain states. It was hypothesized that, during sleep compared to wakefulness, corticocortical functional correlations would decrease. Functional correlation values calculated in a window immediately before the determination of AAT were entered into a linear mixed effects model, allowing multiple arousals across the night per subject into the analysis. The hypothesis was supported using both correlation matrices of brain networks and single seed-region analyses showing whole-brain maps. This represents a novel approach to studying the neuroanatomical correlates of sleep with high spatial resolution by defining sleep in a way that was independent from the conventional sleep stages. This work provides initial evidence to justify searching for sleep stages that are more neuroanatomically localized and unrelated to the conventional sleep stages.

The Na+/K+-ATPase generically enables deterministic bursting in class I neurons by shearing the spike-onset bifurcation structure.

Slow brain rhythms, for example during slow-wave sleep or pathological conditions like seizures and spreading depolarization, can be accompanied by oscillations in extracellular potassium concentration. Such slow brain rhythms typically have a lower frequency than tonic action-potential firing. They are assumed to arise from network-level mechanisms, involving synaptic interactions and delays, or from intrinsically bursting neurons. Neuronal burst generation is commonly attributed to ion channels with slow kinetics. Here, we explore an alternative mechanism generically available to all neurons with class I excitability. It is based on the interplay of fast-spiking voltage dynamics with a one-dimensional slow dynamics of the extracellular potassium concentration, mediated by the activity of the Na+/K+-ATPase. We use bifurcation analysis of the complete system as well as the slow-fast method to reveal that this coupling suffices to generate a hysteresis loop organized around a bistable region that emerges from a saddle-node loop bifurcation-a common feature of class I excitable neurons. Depending on the strength of the Na+/K+-ATPase, bursts are generated from pump-induced shearing the bifurcation structure, spiking is tonic, or cells are silenced via depolarization block. We suggest that transitions between these dynamics can result from disturbances in extracellular potassium regulation, such as glial malfunction or hypoxia affecting the Na+/K+-ATPase activity. The identified minimal mechanistic model outlining the sodium-potassium pump's generic contribution to burst dynamics can, therefore, contribute to a better mechanistic understanding of pathologies such as epilepsy syndromes and, potentially, inform therapeutic strategies.