Sleep Stages Research Articles

Transfer Learning for Automatic Sleep Staging Using a Pre-Gelled Electrode Grid

Novel sensor solutions for sleep monitoring at home could alleviate bottlenecks in sleep medical care as well as enable selective or continuous observation over long periods of time and contribute to new insights in sleep medicine and beyond. Since especially in the latter case the sensor data differ strongly in signal, number and extent of sensors from the classical polysomnography (PSG) sensor technology, an automatic evaluation is essential for the application. However, the training of an automatic algorithm is complicated by the fact that the development phase of the new sensor technology, extensive comparative measurements with standardized reference systems, is often not possible and therefore only small datasets are available. In order to circumvent high system-specific training data requirements, we employ pre-training on large datasets with finetuning on small datasets of new sensor technology to enable automatic sleep phase detection for small test series. By pre-training on publicly available PSG datasets and finetuning on 12 nights recorded with new sensor technology based on a pre-gelled electrode grid to capture electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), an F1 score across all sleep phases of 0.81 is achieved (wake 0.84, N1 0.62, N2 0.81, N3 0.87, REM 0.88), using only EEG and EOG. The analysis additionally considers the spatial distribution of the channels and an approach to approximate classical electrode positions based on specific linear combinations of the new sensor grid channels.

The effects of slow wave sleep characteristics on semantic, episodic, and procedural memory in people with epilepsy

Slow wave sleep (SWS) is highly relevant for verbal and non-verbal/spatial memory in healthy individuals, but also in people with epilepsy. However, contradictory findings exist regarding the effect of seizures on overnight memory retention, particularly relating to procedural and non-verbal memory, and thorough examination of episodic memory retention with ecologically valid tests is missing. This research explores the interaction of SWS duration with epilepsy-relevant factors, as well as the relation of spectral characteristics of SWS on overnight retention of procedural, verbal, and episodic memory. In an epilepsy monitoring unit, epilepsy patients (N = 40) underwent learning, immediate and 12 h delayed testing of memory retention for a fingertapping task (procedural memory), a word-pair task (verbal memory), and an innovative virtual reality task (episodic memory). We used multiple linear regression to examine the impact of SWS duration, spectral characteristics of SWS, seizure occurrence, medication, depression, seizure type, gender, and epilepsy duration on overnight memory retention. Results indicated that none of the candidate variables significantly predicted overnight changes for procedural memory performance. For verbal memory, the occurrence of tonic-clonic seizures negatively impacted memory retention and higher psychoactive medication load showed a tendency for lower verbal memory retention. Episodic memory was significantly impacted by epilepsy duration, displaying a potential nonlinear impact with a longer duration than 10 years negatively affecting memory performance. Higher drug load of anti-seizure medication was by tendency related to better overnight retention of episodic memory. Contrary to expectations longer SWS duration showed a trend towards decreased episodic memory performance. Analyses on associations between memory types and EEG band power during SWS revealed lower alpha-band power in the frontal right region as significant predictor for better episodic memory retention. In conclusion, this research reveals that memory modalities are not equally affected by important epilepsy factors such as duration of epilepsy and medication, as well as SWS spectral characteristics.

Episodic long-term memory formation during slow-wave sleep.

We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words' linguistic processing raised neural complexity. The words' semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.

Episodic long-term memory formation during slow-wave sleep

We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words’ linguistic processing raised neural complexity. The words’ semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.

HUMAN STRESS DETECTION BASED ON SLEEPING HABITS THROUGH MACHINE LEARNING ALGORITHMS

Stress is a mental or emotional state brought on by demanding or unavoidable circumstances, also referred to as stressors. In order to prevent any unfavorable occurrences in life, it is crucial to understand human stress levels. Sleep disturbances are related to a number of physical, mental, and social problems. This study's main objective is to investigate how human stress might be detected using machine learning algorithms based on sleep-related behaviors. Detecting and understanding stress patterns during sleep can provide valuable insights into individuals' overall stress levels and aid in the development of targeted interventions for stress management. This project focuses on the detection of human stress in and through sleep using various sensing modalities and machine learning techniques. The project employs a multimodal approach, combining physiological signals, sleep-related data, and contextual information to capture comprehensive stress patterns during sleep. Physiological signals such as heart rate, electro dermal activity, and respiratory patterns are collected using wearable sensors, while sleep-related data, including sleep stages and sleep quality metrics, are obtained through polysomnography and actigraphy. This data when given to the web application performs the prediction through the ML model and gives the predicted output. This project is made on a Decision Tree Classifier with best hyper parameters. Key Words: Stress, Sleep disturbances, Human stress, Physiological signals, Polysomnography, Actigraphy

Efficacy and Safety of Pulse Intravenous Methylprednisolone in Pediatric Epileptic Encephalopathies: Timing and Networks Consideration

Background: Epileptic encephalopathies (EE) are characterized by severe drug-resistant seizures, early onset, and unfavorable developmental outcomes. This article discusses the use of intravenous methylprednisolone (IVMP) pulse therapy in pediatric patients with EE to evaluate its efficacy and tolerability. Methods: This is a retrospective study from 2020 to 2023. Inclusion criteria were ≤18 years at the time of IVMP pulse therapy and at least 6 months of follow-up. Efficacy and outcome, defined as seizure reduction > 50% (responder rate), were evaluated at 6 and 9 months of therapy, and 6 months after therapy suspension; quality of life (QoL) was also assessed. Variables predicting positive post-IVMP outcomes were identified using statistical analysis. Results: The study included 21 patients, with a responder rate of 85.7% at 6 and 9 months of therapy, and 80.9% at 6 months after therapy suspension. Variables significantly predicting favorable outcome were etiology (p = 0.0475) and epilepsy type (p = 0.0475), with the best outcome achieved in patients with genetic epilepsy and those with encephalopathy related to electrical status epilepticus during slow-wave sleep (ESES). All patients evidenced improvements in QoL at the last follow-up, with no relevant adverse events reported. Conclusions: Our study confirmed the efficacy and high tolerability of IVMP pulse therapy in pediatric patients with EE. Genetic epilepsy and ESES were positive predictors of a favorable clinical outcome. QOL, EEG tracing, and postural–motor development showed an improving trend as well. IVMP pulse therapy should be considered earlier in patients with EE.

Subjective sleep more predictive of global cognitive function than objective sleep in older adults: A specification curve analysis

ObjectivesSleep is associated with cognitive function in older adults. In the current study, we examined this relationship from subjective and objective perspectives, and determined the robustness and dimensional specificity of the associations using a comprehensive modelling approach. MethodsMultiple dimensions of subjective (sleep quality and daytime sleepiness) and objective sleep (sleep stages, sleep parameters, sleep spindles, and slow oscillations), as well as subjectively reported and objectively measured cognitive function were collected from 55 older adults. Specification curve analysis was used to examine the robustness of correlations for the effects of sleep on cognitive function. ResultsRobust associations were found between sleep and objectively measured cognitive function, but not with subjective cognitive complaints. In addition, subjective sleep showed robust and consistent associations with global cognitive function, whereas objective sleep showed a more domain-specific association with episodic memory. Specifically, subjective sleep quality and daytime sleepiness correlated with global cognitive function, and objective sleep parameters correlated with episodic memory. ConclusionsOverall, associations between sleep and cognitive function in older adults depend on how they are measured and which specific dimensions of sleep and domains of cognitive function are considered. It highlights the importance of focusing on specific associations to ameliorate the detrimental effects of sleep disturbance on cognitive function in later life.

Ganoderma lucidum spore extract improves sleep disturbances in a rat model of sporadic Alzheimer’s disease

Introduction:Ganoderma lucidum (G. lucidum, Lingzhi) has long been listed as a premium tonic that can be used to improve restlessness, insomnia, and forgetfulness. We previously reported that a rat model of sporadic Alzheimer’s disease (sAD) that was induced by an intracerebroventricular injection of streptozotocin (ICV-STZ) showed significant learning and cognitive deficits and sleep disturbances. Treatment with a G. lucidum spore extract with the sporoderm removed (RGLS) prevented learning and memory impairments in sAD model rats.Method: The present study was conducted to further elucidate the preventive action of RGLS on sleep disturbances in sAD rats by EEG analysis, immunofluorescence staining, HPLC-MS/MS and Western blot.Results: Treatment with 720 mg/kg RGLS for 14 days significantly improved the reduction of total sleep time, rapid eye movement (REM) sleep time, and non-REM sleep time in sAD rats. The novelty recognition experiment further confirmed that RGLS prevented cognitive impairments in sAD rats. We also found that RGLS inhibited the nuclear factor-κB (NF-κB)/Nod-like receptor family pyrin domain-containing 3 (NLRP3) inflammatory pathway in the medial prefrontal cortex (mPFC) in sAD rats and ameliorated the lower activity of γ-aminobutyric acid (GABA)-ergic neurons in the parabrachial nucleus (PBN).Discussion: These results suggest that inhibiting the neuroinflammatory response in the mPFC may be a mechanism by which RGLS improves cognitive impairment. Additionally, improvements in PBN-GABAergic activity and the suppression of neuroinflammation in the mPFC in sAD rats might be a critical pathway to explain the preventive effects of RGLS on sleep disturbances in sAD.

Single-channel EOG sleep staging on a heterogeneous cohort of subjects with sleep disorders.

Sleep staging based on full polysomnography is the gold standard in the diagnosis of many sleep disorders. It is however costly, complex, and obtrusive due to the use of multiple electrodes. Automatic sleep staging based on single-channel electro-oculography (EOG) is a promising alternative, requiring fewer electrodes which could be self-applied below the hairline. EOG sleep staging algorithms are however yet to be validated in clinical populations with sleep disorders.
Approach. We utilized the SOMNIA dataset, comprising 774 recordings from subjects with various sleep disorders, including insomnia, sleep-disordered breathing, hypersomnolence, circadian rhythm disorders, parasomnias, and movement disorders. The recordings were divided into train (574), validation (100), and test (100) groups. We trained a neural network that integrated transformers within a U-Net backbone. This design facilitated learning of arbitrary-distance temporal relationships within and between the EOG and hypnogram.
Main results. For 5-class sleep staging, we achieved median accuracies of 85.0% and 85.2% and Cohen's kappas of 0.781 and 0.796 for left and right EOG, respectively. The performance using the right EOG was significantly better than using the left EOG, possibly because in the recommended AASM setup, this electrode is located closer to the scalp. The proposed model is robust to the presence of a variety of sleep disorders, displaying no significant difference in performance for subjects with a certain sleep disorder compared to those without.
Significance. The results show that accurate sleep staging using single-channel EOG can be done reliably for subjects with a variety of sleep disorders.

Thalamic epileptic spikes disrupt sleep spindles in patients with epileptic encephalopathy.

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Epileptic encephalopathies share the trait of spike-wave activation during non-rapid eye movement sleep (EE-SWAS), a sleep stage dominated by sleep spindles, brain oscillations known to coordinate offline memory consolidation. Epileptic activity has been proposed to hijack the circuits driving these thalamocortical oscillations, thereby contributing to cognitive impairment. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we provide evidence for epileptic spike interference of thalamic sleep spindle production in patients with EE-SWAS. First, we show that epileptic spikes and sleep spindles are both predicted by slow oscillations during stage two sleep (N2), but at different phases of the slow oscillation. Next, we demonstrate that sleep activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate increases after a cortical spike, p≈0). We then show that epileptic spikes in the thalamus increase the thalamic spindle refractory period (p≈0). Finally, we show that in three patients with EE-SWAS, there is a downregulation of sleep spindles for 30 seconds after each thalamic spike (p<0.01). These direct human thalamocortical observations support a proposed mechanism for epileptiform activity to impact cognitive function, wherein epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy with spike and wave activation during sleep.

Sleep macro-architecture in patients with Parkinson's disease does not change during the first night of neurostimulation in a pilot study.

A growing body of literature suggests that deep brain stimulation (DBS) to treat motor symptoms of Parkinson's disease (PD) may also ameliorate certain sleep deficits. Many foundational studies have examined the impact of stimulation on sleep following several months of therapy, leaving an open question regarding the time course for improvement. It is unknown whether sleep improvement will immediately follow onset of therapy or accrete over a prolonged period of stimulation. The objective of our study was to address this knowledge gap by assessing the impact of DBS on sleep macro-architecture during the first nights of stimulation. Polysomnograms were recorded for three consecutive nights in 14 patients with advanced PD (10 male, 4 female; age: 53-74 years), with intermittent, unilateral subthalamic nucleus DBS on the final night or two. Sleep scoring was determined manually by a consensus of four experts. Sleep macro-architecture was objectively quantified using the percentage, latency, and mean bout length of wake after sleep onset (WASO) and on each stage of sleep (REM and NREM stages N1, N2, N3). Sleep was found to be highly disrupted in all nights. Sleep architecture on nights without stimulation was consistent with prior results in treatment naive patients with PD. No statistically significant difference was observed due to stimulation. These objective measures suggest that one night of intermittent subthreshold stimulation appears insufficient to impact sleep macro-architecture. Name: Adaptive Neurostimulation to Restore Sleep in Parkinson's Disease; URL: https://clinicaltrials.gov/ct2/show/NCT04620551; Identifier: NCT04620551.

A perspective on automated rapid eye movement sleep assessment.

Rapid eye movement sleep is associated with distinct changes in various biomedical signals that can be easily captured during sleep, lending themselves to automated sleep staging using machine learning systems. Here, we provide a perspective on the critical characteristics of biomedical signals associated with rapid eye movement sleep and how they can be exploited for automated sleep assessment. We summarise key historical developments in automated sleep staging systems, having now achieved classification accuracy on par with human expert scorers and their role in the clinical setting. We also discuss rapid eye movement sleep assessment with consumer sleep trackers and its potential for unprecedented sleep assessment on a global scale. We conclude by providing a future outlook of computerised rapid eye movement sleep assessment and the role AI systems may play.

Sleep stage prediction using multimodal body network and circadian rhythm.

Quality sleep plays a vital role in living beings as it contributes extensively to the healing process and the removal of waste products from the body. Poor sleep may lead to depression, memory deficits, heart, and metabolic problems, etc. Sleep usually works in cycles and repeats itself by transitioning into different stages of sleep. This study is unique in that it uses wearable devices to collect multiple parameters from subjects and uses this information to predict sleep stages and sleep patterns. For the multivariate multiclass sleep stage prediction problem, we have experimented with both memoryless (ML) and memory-based models on seven database instances, that is, five from the collected dataset and two from the existing datasets. The Random Forest classifier outclassed the ML models that are LR, MLP, kNN, and SVM with accuracy (ACC) of 0.96 and Cohen Kappa 0.96, and the memory-based model long short-term memory (LSTM) performed well on all the datasets with the maximum attained accuracy of 0.88 and Kappa 0.82. The proposed methodology was also validated on a longitudinal dataset, the Multiethnic Study of Atherosclerosis (MESA), with ACC and Kappa of 0.75 and 0.64 for ML models and 0.86 and 0.78 for memory-based models, respectively, and from another benchmarked Apple Watch dataset available on Physio-Net with ACC and Kappa of 0.93 and 0.93 for ML and 0.92 and 0.87 for memory-based models, respectively. The given methodology showed better results than the original work and indicates that the memory-based method works better to capture the sleep pattern.

Comparing targeted memory reactivationduring slow wave sleep and sleep stage 2.

Sleep facilitates declarative memory consolidation, which is assumed to rely on the reactivation of newly encoded memories orchestrated by the temporal interplay of slow oscillations (SO), fast spindles and ripples. SO as well as the number of spindles coupled to SO are more frequent during slow wave sleep (SWS) compared to lighter sleep stage 2 (S2). But, it is unclear whether memory reactivation is more effective during SWS than during S2. To test this question, we applied Targeted Memory Reactivation (TMR) in a declarative memory design by presenting learning-associated sound cues during SWS vs. S2 in a counterbalanced within-subject design. Contrary to our hypothesis, memory performance was not significantly better when cues were presented during SWS. Event-related potential (ERP) amplitudes were significantly higher for cues presented during SWS than S2, and the density of SO and SO-spindle complexes was generally higher during SWS than during S2. Whereas SO density increased during and after the TMR period, SO-spindle complexes decreased. None of the parameters were associated with memory performance. These findings suggest that the efficacy of TMR does not depend on whether it is administered during SWS or S2, despite differential processing of memory cues in these sleep stages.

0724 Wake Intrusion Index During NREM Sleep Is Associated with Comorbid Insomnia and Sleep Apnea Exclusively in Men

0724 Wake Intrusion Index During NREM Sleep Is Associated with Comorbid Insomnia and Sleep Apnea Exclusively in Men

0365 Failure to Reduce Glutamate Levels in Medial Prefrontal Cortex During NREM Sleep Could Cause Transient Insomnia

0365 Failure to Reduce Glutamate Levels in Medial Prefrontal Cortex During NREM Sleep Could Cause Transient Insomnia

0138 Effects of Slow Oscillation Enhancement During NREM Sleep Using Closed-loop Auditory Stimulation on Anxiety

0138 Effects of Slow Oscillation Enhancement During NREM Sleep Using Closed-loop Auditory Stimulation on Anxiety

0553 Changes in QTc and QT Variability in Patients with OSA with and Without Heart Failure with PAP Based on Sleep Stage

0553 Changes in QTc and QT Variability in Patients with OSA with and Without Heart Failure with PAP Based on Sleep Stage

0677 Winding down for the Night: Changes in Thalamocortical Connectivity Before Bed Are Associated with Deeper Sleep

0677 Winding down for the Night: Changes in Thalamocortical Connectivity Before Bed Are Associated with Deeper Sleep

0092 Influence of Evening Alcohol Consumption on Nocturnal Cardiac Reactivity During NREM vs REM Sleep

0092 Influence of Evening Alcohol Consumption on Nocturnal Cardiac Reactivity During NREM vs REM Sleep