A Review of Sleep EEG Patterns. Part I: A Compilation of Amended Rules for Their Visual Recognition according to Rechtschaffen and Kales. Eine �bersicht �ber Schlaf-EEG-Muster. Teil I: Eine Zusammenstellung mit erg�nzenden Regeln zu deren visueller Analyse

Pitfalls and possibilities of using Root SedLine for continuous assessment of EEG waveform-based metrics in intensive care research

Objective.The Root SedLine device is used for continuous electroencephalography (cEEG)-based sedation monitoring in intensive care patients. The cEEG traces can be collected for further processing and calculation of relevant metrics not already provided. Depending on the device settings during acquisition, the acquired traces may be distorted by max/min value cropping or high digitization errors. We aimed to systematically assess the impact of these distortions on metrics used for clinical research in the field of neuromonitoring.Approach.A 16 h cEEG acquired using the Root SedLine device at the optimal screen settings was analyzed. Cropping and digitization error effects were simulated by consecutive reduction of the maximum cEEG amplitude by 2µV or by reducing the vertical resolution. Metrics were calculated within ICM+ using minute-by-minute data, including the total power, alpha delta ratio (ADR), and 95% spectral edge frequency. Data were analyzed by creating violin- or box-plots.Main Results.Cropping led to a continuous reduction in total and band power, leading to corresponding changes in variability thereof. The relative power and ADR were less affected. Changes in resolution led to relevant changes. While the total power and power of low frequencies were rather stable, the power of higher frequencies increased with reducing resolution.Significance.Care must be taken when acquiring and analyzing cEEG waveforms from Root SedLine for clinical research. To retrieve good quality metrics, the screen settings must be kept within the central vertical scale, while pre-processing techniques must be applied to exclude unacceptable periods.

Does the brain sleep differently depending on intellectual abilities?

AIMS

To compare the children's sleep electroencephalogram according to their intellectual profile.

METHODS

Children were grouped according to their Wechsler Intelligence Scale for Children (WISC) scores (17 with normal intelligence quotient [IQ, NIQ] and 24 with high IQ [HIQ]). Comparisons of spectral power between groups and its relationship with WISC scores were assessed using analyses of variance and linear regression models, adjusted for age and sex.

RESULTS

Children with HIQ had more rapid eye movement (REM) sleep, especially late at night, and more power in slow-frequency bands during REM sleep than those with NIQ. There were also positive associations between the processing speed index and the spectral power in β bands in NREM sleep, and with the spectral power in α, σ, β, and γ bands in REM sleep, with different associations between groups.

CONCLUSION

The enhanced power in slow bands during REM sleep in children with HIQ overlaps with that of typical REM sleep oscillations thought to be involved in emotional memory consolidation. The dissimilar relationships between spectral power and WISC scores in NIQ and HIQ groups may underlie functional differences in brain activity related to cognitive efficiency, questioning the direction of the relationship between sleep and cognitive functioning.

Orexin Receptor Antagonism: Normalizing Sleep Architecture in Old Age and Disease

Sleep is essential for human well-being, yet the quality and quantity of sleep reduce as age advances. Older persons (>65 years old) are more at risk of disorders accompanied and/or exacerbated by poor sleep. Furthermore, evidence supports a bidirectional relationship between disrupted sleep and Alzheimer's disease (AD) or related dementias. Orexin/hypocretin neuropeptides stabilize wakefulness, and several orexin receptor antagonists (ORAs) are approved for the treatment of insomnia in adults. Dysregulation of the orexin system occurs in aging and AD, positioning ORAs as advantageous for these populations. Indeed, several clinical studies indicate that ORAs are efficacious hypnotics in older persons and dementia patients and, as in adults, are generally well tolerated. ORAs are likely to be more effective when administered early in sleep/wake dysregulation to reestablish good sleep/wake-related behaviors and reduce the accumulation of dementia-associated proteinopathic substrates. Improving sleep in aging and dementia represents a tremendous opportunity to benefit patients, caregivers, and health systems.

EEG wakefulness regulation in transdiagnostic patients after a recent suicide attempt

OBJECTIVE

Decades of research have not yet produced statistically reliable predictors of preparatory behavior eventually leading to suicide attempts or deaths by suicide. As the nature of suicidal behavior is complex, it is best investigated in a transdiagnostic approach, while assessing objective markers, as proposed by the Research Domain Criteria (Cuthbert, 2013).

METHODS

A 15-min resting-state EEG was recorded in 45 healthy controls, and 49 transdiagnostic in-patients with a recent (<6 months) suicide attempt. Brain arousal regulation in eyes-closed condition was assessed with the Vigilance Algorithm Leipzig (VIGALL) (Sander et al., 2015).

RESULTS

A significant incline of median vigilance and vigilance slope was observed in patients within the first 3-min of the EEG recording. Additionally, a significant positive correlation of self-reported suicidal ideation with the vigilance slope over 15-min recording time, as well as a significant negative correlation with EEG vigilance stage A1 during the first 3-min was found.

CONCLUSIONS

Transdiagnostic patients with a recent suicide attempt show a distinct vigilance regulation pattern. Further studies including a control group consisting of patients without life-time suicide attempts are needed to increase the clinical utility of the findings.

SIGNIFICANCE

These findings might serve as potential objective markers of suicidal behavior.

Characterizing brain stage-dependent pupil dynamics based on lateral hypothalamic activity

Pupil dynamics presents varied correlation features with brain activity under different vigilant levels. The modulation of brain dynamic stages can arise from the lateral hypothalamus (LH), where diverse neuronal cell types contribute to arousal regulation in opposite directions via the anterior cingulate cortex (ACC). However, the relationship of the LH and pupil dynamics has seldom been investigated. Here, we performed local field potential (LFP) recordings at the LH and ACC, and whole-brain fMRI with simultaneous fiber photometry Ca2+ recording in the ACC, to evaluate their correlation with brain state-dependent pupil dynamics. Both LFP and functional magnetic resonance imaging (fMRI) data showed various correlations to pupil dynamics across trials that span negative, null, and positive correlation values, demonstrating brain state-dependent coupling features. Our results indicate that the correlation of pupil dynamics with ACC LFP and whole-brain fMRI signals depends on LH activity, suggesting a role of the latter in brain dynamic stage regulation.

Deep-spindle: An automated sleep spindle detection system for analysis of infant sleep spindles

BACKGROUND

Sleep spindles are an indicator of the development and integrity of the central nervous system in infants. Identifying sleep spindles manually in EEG is time-consuming and typically requires experienced experts. Automated detection of sleep spindles would greatly facilitate this analysis. Deep learning methods have been widely used recently in EEG analysis.

METHOD

We have developed a deep learning-based automated sleep spindle detection system, Deep-spindle, which employs a convolutional neural network (CNN) combined with a bidirectional Long Short-Term Memory (LSTM) network, which could assist in the analysis of infant sleep spindles. Deep-spindle was trained on the EEGs of ex-term infants to estimate the number and duration of sleep spindles. The ex-term EEG on channel F4-C4 was split into training (N=81) and validation (N=30) sets. An additional 30 ex-term EEG and 54 ex-preterm infant EEGs (channel F4-C4 and F3-C3) were used as an independent test set.

RESULT

Deep-spindle detected the number of sleep spindles with 91.9% to 96.5% sensitivity and 95.3% to 96.7% specificity, and estimated sleep spindle duration with a percent error of 13.1% to 19.1% in the independent test set. For each detected spindle event, the user is presented with amplitude, power spectral density and the spectrogram of the corresponding spindle EEG, and the probability of the event being a sleep spindle event, providing the user with insight into why the event is predicted as a sleep spindle to provide confidence in the predictions.

CONCLUSION

The Deep-spindle system can reduce physicians' workload, demonstrating the potential to assist physicians in the automated analysis of sleep spindles in infants.

Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence

An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG signals. In this research work, an empirical mode decomposition is used in combination with stacked autoencoders to conduct automatic sleep stage classification with reliable analytical performance. Due to the decomposition of the composite signal into several intrinsic mode functions, empirical mode decomposition offers an effective solution for denoising non-stationary signals such as EEG. Preliminary results showed that through these intrinsic modes, a signal with a high signal-to-noise ratio can be obtained, which can be used for further analysis with confidence. Therefore, later, when statistical features were extracted from the denoised signals and were classified using stacked autoencoders, improved results were obtained for Stage 1, Stage 2, Stage 3, Stage 4, and REM stage EEG signals using this combination.

Association between early stage N2 sleep spindle burst characteristics and vigilance groups: an observational study on patients from a tertiary sleep centre

OBJECTIVE

Obstructive Sleep Apnoea (OSA) is associated with impaired vigilance. This paper examines the hypothesis that sleep spindle (Sp) characteristics during nocturnal sleep can be mapped to vigilance deficits measured by the Psychomotor Vigilance Task (PVT) in patients with OSA.

APPROACH

The PVT was performed prior to In-laboratory Polysomnography for 250 patients. PVT outcomes were clustered into three Vigilance Groups (VGs). Spindles were scored manually for a Training Cohort of 55 patients, (9491 Sps) across different blocks of NREM sleep (SBs) and validated in a Test Cohort (25 patients, 4867 Sps). We proposed a novel set of Sp features including a Spindle Burst Index (SBI), which quantifies the burst characteristics of spindles and constructed models mapping them to VGs. We also explored the performance of conventional Sp features (such as Sp number and density) in our modelling approach.

MAIN RESULTS

In the Training Cohort, we observed statistically significant differences in the SBI across VGs and SBs independent of OSA severity (1st Stage N2 SBI; p=<0.001 across VGs). In the Test Cohort, a Model based on the proposed SBI predicted VG membership with 88% accuracy. A model based on conventional Sp features mapped to VGs with 70.7% accuracy, and a model using mixed burst and conventional features reached an accuracy of 88%.

SIGNIFICANCE

Spindle features measured during diagnostic In-laboratory PSG can be mapped to PVT outcomes. The novel SBI proved useful for exploring the relationship between PVT outcomes and sleep. Further studies in larger populations are needed to verify these conclusions.

Objective sleep assessments for healthy people in environmental research: A literature review

To date, although many studies had focused on the impact of environmental factors on sleep, how to choose the proper assessment method for objective sleep quality was often ignored, especially for healthy subjects in bedroom environment. In order to provide methodological guidance for future research, this paper reviewed the assessments of objective sleep quality applied in environmental researches, compared them from the perspective of accuracy and interference, and statistically analyzed the impact of experimental type and subjects' information on method selection. The review results showed that, in contrast to polysomnography (PSG), the accuracy of actigraphy (ACT), respiratory monitoring-oxygen saturation monitoring (RM-OSM), and electrocardiograph (ECG) could reach up to 97%, 80.38%, and 79.95%, respectively. In terms of sleep staging, PSG and ECG performed the best, ACT the second, and RM-OSM the worst; as compared to single methods, mix methods were more accurate and better at sleep staging. PSG interfered with sleep a great deal, while ECG and ACT could be non-contact, and thus, the least interference with sleep was present. The type of experiment significantly influenced the choice of assessment method (p < 0.001), 85.3% of researchers chose PSG in laboratory study while 82.5% ACT in field study; moreover, PSG was often used in a relatively small number of young subjects, while ACT had a wide applicable population. In general, researchers need to pay more attention at selection of assessments in future studies, and this review can be used as a reliable reference for experimental design.

Mapping Sleep Spindle Characteristics to Vigilance Outcomes in Patients with Obstructive Sleep Apnea

Obstructive Sleep Apnea (OSA) is a sleep disorder associated with reduced vigilance. Vigilance status is often measured using the Psychomotor Vigilance Task (PVT). This paper investigates modelling strategies to map sleep spindle (Sp) characteristics to PVT metrics in patients with OSA. Sleep spindles (n=2305) were manually detected across blocks of sleep for 20 patients randomly selected from a cohort of 190 undergoing Polysomnography (PSG) for suspected OSA. Novel Sp metrics based on runs or "bursts" of Sps were used to model Sp characteristics to standardized (z) Lapse and Median Reaction Time (MdRT) scores, and to Groups based on zLapse and zMdRT scores. A model employing Sp Burst characteristics mapped to MdRT Group membership with an accuracy of 91.9%, (95% C.I. 90.8-93.0). The model had a sensitivity of 88.9%, (95% C.I. 87.5-89.0) and specificity of 89.1% (95% C.I. 87.3-90.5) for detecting patients with the lowest MdRTs in our cohort.Clinical Relevance- Based on these results it may be possible to use Sp data collected during overnight diagnostic PSG for OSA to detect patients at risk for attention deficits. This would improve triage for OSA therapy by identifying at risk patients at the time of OSA diagnosis and would remove the need to employ additional testing to assess vigilance status.

A Novel Sleep Staging Network Based on Data Adaptation and Multimodal Fusion

Sleep staging is one of the important methods to diagnosis and treatment of sleep diseases. However, it is laborious and time-consuming, therefore, computer assisted sleep staging is necessary. Most of the existing sleep staging researches using hand-engineered features rely on prior knowledges of sleep analysis, and usually single channel electroencephalogram (EEG) is used for sleep staging task. Prior knowledge is not always available, and single channel EEG signal cannot fully represent the patient’s sleeping physiological states. To tackle the above two problems, we propose an automatic sleep staging network model based on data adaptation and multimodal feature fusion using EEG and electrooculogram (EOG) signals. 3D-CNN is used to extract the time-frequency features of EEG at different time scales, and LSTM is used to learn the frequency evolution of EOG. The nonlinear relationship between the High-layer features of EEG and EOG is fitted by deep probabilistic network. Experiments on SLEEP-EDF and a private dataset show that the proposed model achieves state-of-the-art performance. Moreover, the prediction result is in accordance with that from the expert diagnosis.

Sleepy Surgeons: A Multi-Method Assessment of Sleep Deprivation and Performance in Surgery

BACKGROUND

Minimum rest is mandated in high stake industries such as aviation. The current system of healthcare provision permits on-call surgeons to work in sleep deprived states when performing procedures. Fatigue has been demonstrated to negatively affect performance. This study aimed to explore measurements of sleep deprivation and their impact on simulated performance.

METHODS

This was a single site study conducted between September 2019 and February 2020. Surgical trainee and consultants were conveniently sampled from a single site. All testing was done between 7 AM and 9 AM. Participants completed electroencephalogram testing using a modified Multiple Sleep Latency Test testing for objective sleep measurement, the Pittsburgh Sleep Quality Index, Chalder Fatigue Scale and Epworth Sleepiness Scale for subjective sleep measurement. The Psychomotor Vigilance Task and the SIMENDO simulated tasks were used for standardized performance assessment.

RESULTS

Surgeons entered sleep in 6 min, on average pre-call. This significantly decreased to an average of 164 s post-call (P = 0.016). Pittsburgh Sleep Quality Index scoring was 5, indicating poor baseline sleep quality. There was higher self-reported fatigue and sleepiness in post-call states. Performance decrements were noted in cognitive performance reaction time and aspects of technical instrument proficiency.

CONCLUSIONS

Surgeons are objectively sleep deprived pre-call according to internationally recognized guidelines. This sleep deprivation increases significantly in post-call states. Tasks with higher cognitive demands showed greater levels of diminished performance compared to those with lower cognitive demands. Current models of provision of surgical on-call are not conducive to optimizing sleep in surgeons. Prioritization of workload in post-call states, focusing on preserving individuals cognitive resources and utilizing lower cognitively demanding aspects of work is likely to have positive impacts on performance outcomes.

Spindle-AI: Sleep spindle number and duration estimation in infant EEG

OBJECTIVE

Sleep spindle features show developmental changes during infancy and have the potential to provide an early biomarker for abnormal brain maturation. Manual identification of sleep spindles in the electroencephalogram (EEG) is time-consuming and typically requires highly-trained experts. Automated detection of sleep spindles would greatly facilitate this analysis. Research on the automatic detection of sleep spindles in infant EEG has been limited to-date.

METHODS

We present a random forest-based sleep spindle detection method (Spindle-AI) to estimate the number and duration of sleep spindles in EEG collected from 141 ex-term born infants, recorded at 4 months of age. The signal on channel F4-C4 was split into a training set (81 ex-term) and a validation set (30 ex-term). An additional 30 ex-term infant EEGs (channel F4-C4 and channel F3-C3) were used as an independent test set. Fourteen features were selected for input into a random forest algorithm to estimate the number and duration of spindles and the results were compared against sleep spindles annotated by an experienced clinical physiologist.

RESULTS

The prediction of the number of sleep spindles in the independent test set demonstrated 93.3% to 93.9% sensitivity, 90.7% to 91.5% specificity, and 89.2% to 90.1% precision. The duration estimation of sleep spindle events in the independent test set showed a percent error of 5.7% to 7.4%.

CONCLUSION AND SIGNIFICANCE

Spindle-AI has been implemented as a web server that has the potential to assist clinicians in the fast and accurate monitoring of sleep spindles in infant EEGs.

EEG-Based Sleep Staging Analysis with Functional Connectivity

Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.

Random Forest-based Algorithm for Sleep Spindle Detection in Infant EEG

Sleep spindles are associated with normal brain development, memory consolidation and infant sleep-dependent brain plasticity and can be used by clinicians in the assessment of brain development in infants. Sleep spindles can be detected in EEG, however, identifying sleep spindles in EEG recordings manually is very time-consuming and typically requires highly trained experts. Research on the automatic detection of sleep spindles in infant EEGs has been limited to-date. In this study, we present a novel supervised machine learning-based algorithm to detect sleep spindles in infant EEG recordings. EEGs collected from 141 ex-term born infants and 6 ex-preterm born infants, recorded at 4 months of age (adjusted), were used to train and test the algorithm. Sleep spindles were annotated by experienced clinical physiologists as the gold standard. The dataset was split into training (81 ex-term), validation (30 ex-term), and testing (30 ex-term + 6 ex-preterm) set. 15 features were selected for input into a random forest algorithm. Sleep spindles were detected in the ex-term infant EEG test set with 92.1% sensitivity and 95.2% specificity. For ex-preterm born infants, the sensitivity and specificity were 80.3% and 91.8% respectively. The proposed algorithm has the potential to assist researchers and clinicians in the automated analysis of sleep spindles in infant EEG.

K-complexes Detection in EEG Signals using Fractal and Frequency Features Coupled with an Ensemble Classification Model

K-complexes are important transient bio-signal waveforms in sleep stage 2. Detecting k-complexes visually requires a highly qualified expert. In this study, an efficient method for detecting k-complexes from electroencephalogram (EEG) signals based on fractal and frequency features coupled with an ensemble model of three classifiers is presented. EEG signals are first partitioned into segments, using a sliding window technique. Then, each EEG segment is decomposed using a dual-tree complex wavelet transform (DT-CWT) to a set of real and imaginary parts. A total of 10 sub-bands are used based on four levels of decomposition, and the high sub-bands are considered in this research for feature extraction. Fractal and frequency features based on DT-CWT and Higuchi's algorithm are pulled out from each sub-band and then forwarded to an ensemble classifier to detect k-complexes. A twelve-feature set is finally used to detect the sleep EEG characteristics using the ensemble model. The ensemble model is designed using a combination of three classification techniques including a least square support vector machine (LS-SVM), k-means and Naïve Bayes. The proposed method for the detection of the k-complexes achieves an average accuracy rate of 97.3 %. The results from the ensemble classifier were compared with those by individual classifiers. Comparisons were also made with existing k-complexes detection approaches for which the same datasets were used. The results demonstrate that the proposed approach is efficient in identifying the k-complexes in EEG signals; it yields optimal results with a window size 0.5 s. It can be an effective tool for sleep stages classification and can be useful for doctors and neurologists for diagnosing sleep disorders.

The Emergence of Spindles and K-Complexes and the Role of the Dorsal Caudal Part of the Anterior Cingulate as the Generator of K-Complexes

The large multicomponent K-complex (KC) and the rhythmic spindle are the hallmarks of non-rapid eye movement (NREM)-2 sleep stage. We studied with magnetoencephalography (MEG) the progress of light sleep (NREM-1 and NREM-2) and emergence of KCs and spindles. Seven periods of interest (POI) were analyzed: wakefulness, the two quiet "core" periods of light sleep (periods free from any prominent phasic or oscillatory events) and four periods before and during spindles and KCs. For each POI, eight 2-s (1250 time slices) segments were used. We employed magnetic field tomography (MFT) to extract an independent tomographic estimate of brain activity from each MEG data sample. The spectral power was then computed for each voxel in the brain for each segment of each POI. The sets of eight maps from two POIs were contrasted using a voxel-by-voxel t-test. Only increased spectral power was identified in the four key contrasts between POIs before and during spindles and KCs versus the NREM2 core. Common increases were identified for all four subjects, especially within and close to the anterior cingulate cortex (ACC). These common increases were widespread for low frequencies, while for higher frequencies they were focal, confined to specific brain areas. For the pre-KC POI, only one prominent increase was identified, confined to the theta/alpha bands in a small area in the dorsal caudal part of ACC (dcACC). During KCs, the activity in this area grows in intensity and extent (in space and frequency), filling the space between the areas that expanded their low frequency activity (in the delta band) during NREM2 compared to NREM1. Our main finding is that prominent spectral power increases before NREM2 graphoelements are confined to the dcACC, and only for KCs, sharing common features with changes of activity in dcACC of the well-studied error related negativity (ERN). ERN is seen in awake state, in perceptual conflict and situations where there is a difference between expected and actual environmental or internal events. These results suggest that a KC is the sleep side of the awake state ERN, both serving their putative sentinel roles in the frame of the saliency network.

EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces

The Electroencephalography (EEG) is not just a mere clinical tool anymore. It has become the de-facto mobile, portable, non-invasive brain imaging sensor to harness brain information in real time. It is now being used to translate or decode brain signals, to diagnose diseases or to implement Brain Computer Interface (BCI) devices. The automatic decoding is mainly implemented by using quantitative algorithms to detect the cloaked information buried in the signal. However, clinical EEG is based intensively on waveforms and the structure of signal plots. Hence, the purpose of this work is to establish a bridge to fill this gap by reviewing and describing the procedures that have been used to detect patterns in the electroencephalographic waveforms, benchmarking them on a controlled pseudo-real dataset of a P300-Based BCI Speller and verifying their performance on a public dataset of a BCI Competition.

Relevance of electroencephalogram assessment in amyloid and tau pathology in rat

In order to contribute to a better knowledge on the relationship between amyloid and tau pathology, and electroencephalography (EEG) disturbances, the aim of this study was to evaluate the effects of injection of beta amyloid Abeta(1-42) peptide, tau (a recombinant AAV (Adeno-Associated Virus) containing the human transgene tau with the P301 L mutation on rats and the combination of both, on the power of brain's rhythm (delta, theta, alpha, beta and gamma waves) during the different sleep/wake states of animals by EEG recording. Currently, no preclinical studies explore the effect of the tau pathology on EEG. The experimentations were performed 3 weeks and 3 months post injections. Beta amyloid deposits and hyperphosphorylated Tau are observed by immunohistofluorescence, only in the hippocampus. Furthermore, using a radial arm water maze, the main effect was observed on working memory which was significantly impaired in Abeta-Tau group only 3 months post injections. However, on EEG, as early as the 3^rd week, an overall decrease of the EEG bands power was observed in the treated groups, particularly the theta waves during the rapid eye movement (REM) sleep. Beta amyloid was mainly involved in these perturbations. Obviously, EEG seems to be an interesting tool in the early diagnostic of amyloid and tau pathologies, with a good sensitivity and the possibility to perform a follow up during a large period.

Tracking wakefulness as it fades: Micro-measures of alertness

A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments under eyes-closed settings. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method (micro-measures algorithm) based on frequency and sleep graphoelements, which are capable of detecting micro variations in alertness. The validity of the micro-measures algorithm was verified by training and testing using a dataset where participants are known to fall asleep. In addition, we tested generalisability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience under eyes-closed settings.

An Ambulatory Electroencephalography System for Freely Moving Horses: An Innovating Approach

Electroencephalography (EEG) that has been extensively studied in humans presents also a large interest for studies on animal brain processes. However, since the quality of the recordings is altered by muscular activity, most EEG recordings on animals are obtained using invasive methods with deeply implanted electrodes. This requires anesthesia and can thus only be used in laboratory or clinical settings. As EEG is a very useful tool both for detecting brain alterations due to diseases or accidents and to evaluate the arousal and attentional state of the animal, it seemed crucial to develop a tool that would make such recordings possible in the horse’s home environment, with a freely moving horse. Such a tool should neither be invasive nor cause discomforts to the horse as the usual other practice which consists, after shaving the zone, in gluing the electrodes to the skin. To fulfill these requirements, we developed a novel EEG headset adapted to the horse’s head that allows an easy and fast positioning of the electrodes and that can be used in the home environment on a freely moving horse. In this study, we show that this EEG headset allows to obtain reliable recordings, and we propose an original evaluation of an animal’s “EEG profile” that allows comparisons between individuals and situations. This EEG headset opens new possibilities of investigation on horse cognition, and it can also become a useful tool for veterinarians to evaluate cerebral disorders or check the anesthesia level during a surgery.

Spontaneous K-Complex Density in Slow-Wave Sleep

PURPOSE

To study spontaneous K-complex (KC) densities during slow-wave sleep. The secondary objective was to estimate intra-non-rapid eye movement (NREM) sleep differences in KC density.

MATERIALS AND METHODS

It is a retrospective study using EEG data included in polysomnographic records from the archive at the sleep research laboratory of the Centre for Physiotherapy and Rehabilitation Sciences, Jamia Millia Islamia, India. The EEG records of 4459 minutes were used. The study presents a manual identification investigation of KCs in 17 healthy young adult male volunteers (age = 23.82±3.40 years and BMI = 23.42±4.18 kg/m2).

RESULTS

N3 had a higher KC density than N2 (Z = -2.485, p = 0.013) for all of the probes taken together. Four EEG probes had a higher probe-specific KC density during N3. The inter-probe KC density differed significantly during N2 (χ2 = 67.91, p < .001), N3 (χ2 = 70.62, p < .001) and NREM (χ2 = 68.50, p < .001). The percent distribution of KC decreased uniformly with sleep cycles.

CONCLUSION

The inter-probe differences during N3 establish the fronto-central dominance of the KC density regardless of sleep stage. This finding supports one local theory of KC generation. The significantly higher KC density during N3 may imply that the neuro-anatomical origin of slow-wave activity and KC is the same. This temporal alignment with slow-wave activity supports the sleep-promoting function of the KC.

Connectivity Measures in EEG Microstructural Sleep Elements

During Non-Rapid Eye Movement sleep (NREM) the brain is relatively disconnected from the environment, while connectedness between brain areas is also decreased. Evidence indicates, that these dynamic connectivity changes are delivered by microstructural elements of sleep: short periods of environmental stimuli evaluation followed by sleep promoting procedures. The connectivity patterns of the latter, among other aspects of sleep microstructure, are still to be fully elucidated. We suggest here a methodology for the assessment and investigation of the connectivity patterns of EEG microstructural elements, such as sleep spindles. The methodology combines techniques in the preprocessing, estimation, error assessing and visualization of results levels in order to allow the detailed examination of the connectivity aspects (levels and directionality of information flow) over frequency and time with notable resolution, while dealing with the volume conduction and EEG reference assessment. The high temporal and frequency resolution of the methodology will allow the association between the microelements and the dynamically forming networks that characterize them, and consequently possibly reveal aspects of the EEG microstructure. The proposed methodology is initially tested on artificially generated signals for proof of concept and subsequently applied to real EEG recordings via a custom built MATLAB-based tool developed for such studies. Preliminary results from 843 fast sleep spindles recorded in whole night sleep of 5 healthy volunteers indicate a prevailing pattern of interactions between centroparietal and frontal regions. We demonstrate hereby, an opening to our knowledge attempt to estimate the scalp EEG connectivity that characterizes fast sleep spindles via an "EEG-element connectivity" methodology we propose. The application of the latter, via a computational tool we developed suggests it is able to investigate the connectivity patterns related to the occurrence of EEG microstructural elements. Network characterization of specified physiological or pathological EEG microstructural elements can potentially be of great importance in the understanding, identification, and prediction of health and disease.

Insomnia Characterization: From Hypnogram to Graph Spectral Theory

OBJECTIVE

To quantify and differentiate control and insomnia sleep onset patterns through biomedical signal processing of overnight polysomnograms.

METHODS

The approach consisted of three tandem modules: 1) biosignal processing module, which used state-space time-varying autoregressive moving average (TVARMA) processes with recursive particle filter, 2) hypnogram generation module that implemented a fuzzy inference system (FIS), and 3) insomnia characterization module that discriminated between control and subjects with insomnia using a logistic regression model trained with a set of similarity measures ( d1, d2 , d3, d4). The study employed sleep onset periods from 16 control and 16 subjects with insomnia.

RESULTS

State-spaced TVARMA processes with recursive particle filtering provided resilience to nonlinear, nonstationary, and non-Gaussian conditions of biosignals. FIS managed automated sleep scoring robust to intersubjects' and interraters' variability. The similarity distances quantified in a scalar measure the transitions amongst sleep onset stages, computed from expert and automated hypnograms. A statistical set of unpaired two-tailed t -tests suggested that distances d1 , d2, and d3 had larger statistical significance ( ) to characterize sleeping patterns. The logistic regression model classified control and subjects with insomnia with sensitivity 87 % , specificity 75 %, and accuracy 81 %.

CONCLUSION

Our approach can perform a supportive role in either biosignal processing, sleep staging, insomnia characterization, or all the previous, coping with time-consuming procedures and massive data volumes of standard protocols.

SIGNIFICANCE

The introduction of graph spectral theory and logistic regression for the diagnosis of insomnia represents a novel concept, attempting to describe complex sleep dynamics throughout transitions networks and scalar measures.

Sleep assessment of hospitalised patients: a literature review

BACKGROUND

Sleep is a dynamic and essential part of human life and health. In healthcare settings, nurses are strategically placed to promote sleep and sleep health. In this regard, nursing actions should be based upon effective methods of assessment of patient sleep. Standardised sleep assessment does not currently occur in the care of acute hospitalised patients. Use of an appropriate measurement tool would help evaluate inpatient sleep. An effective, efficient sleep assessment tool is needed to aid clinicians. Such assessment would enable specific nursing intervention to be tailored to individual patients.

OBJECTIVE

The objective of this paper was to examine the literature on sleep measurement to identify subjective sleep assessment tools that may be suitable for routine use with hospitalised patients, and to evaluate their reliability and validity.

METHOD

A review of existing literature was undertaken to identify and evaluate subjective sleep measurement tools.

RESULTS

The initial literature searches identified 402 articles, of which ten met the criteria for review. These reported on three subjective sleep measurement scales: the Richards-Campbell Sleep Questionnaire; the St Mary's Hospital Sleep Questionnaire; and the Verran Snyder-Halpern Sleep Scale. The Richards-Campbell Sleep Questionnaire is brief and easy to use. In specific samples, its items correlate with domains reflecting sleep quality and has shown excellent internal consistency. Equivocal results and scoring challenges were found with the St Mary's Hospital Sleep Questionnaire. The Verran Snyder-Halpern Sleep Scale captured sleep disturbance and total sleep time, but time-to-complete is more burdensome than the Richards-Campbell Sleep Questionnaire.

CONCLUSIONS

The current use of sleep assessment instruments in the acute hospital setting is restricted mainly to research activities. Of the three tools identified that could be used clinically to measure inpatient sleep, and although it was developed for use in the intensive care setting, the Richards-Campbell Sleep Questionnaire held greatest potential due to its ease and rapidity of use. However, it has yet to be validated for use with general hospital inpatients, and further research is required in this area.

Guidelines for the recording and evaluation of pharmaco-sleep studies in man: the International Pharmaco-EEG Society (IPEG)

The International Pharmaco-EEG Society (IPEG) presents guidelines summarising the requirements for the recording and computerised evaluation of pharmaco-sleep data in man. Over the past years, technical and data-processing methods have advanced steadily, thus enhancing data quality and expanding the palette of sleep assessment tools that can be used to investigate the activity of drugs on the central nervous system (CNS), determine the time course of effects and pharmacodynamic properties of novel therapeutics, hence enabling the study of the pharmacokinetic/pharmacodynamic relationship, and evaluate the CNS penetration or toxicity of compounds. However, despite the presence of robust guidelines on the scoring of polysomnography -recordings, a review of the literature reveals inconsistent -aspects in the operating procedures from one study to another. While this fact does not invalidate results, the lack of standardisation constitutes a regrettable shortcoming, especially in the context of drug development programmes. The present guidelines are intended to assist investigators, who are using pharmaco-sleep measures in clinical research, in an effort to provide clear and concise recommendations and thereby to standardise methodology and facilitate comparability of data across laboratories.

A probabilistic framework for a physiological representation of dynamically evolving sleep state

This work presents a probabilistic method for mapping human sleep electroencephalogram (EEG) signals onto a state space based on a biologically plausible mathematical model of the cortex. From a noninvasive EEG signal, this method produces physiologically meaningful pathways of the cortical state over a night of sleep. We propose ways in which these pathways offer insights into sleep-related conditions, functions, and complex pathologies. To address explicitly the noisiness of the EEG signal and the stochastic nature of the mathematical model, we use a probabilistic Bayesian framework to map each EEG epoch to a distribution of likelihoods over all model sleep states. We show that the mapping produced from human data robustly separates rapid eye movement sleep (REM) from slow wave sleep (SWS). A Hidden Markov Model (HMM) is incorporated to improve the path results using the prior knowledge that cortical physiology has temporal continuity.

Spindle power is not affected after spontaneous K-complexes during human NREM sleep

K-complexes and sleep spindles often grouped together characterize the second stage of NREM sleep and interest has been raised on a possible interaction of their underlying mechanisms. The reported inhibition of spindles power for about 15 seconds following evoked K-complexes has implications on their role in arousal. Our objective was to assess this inhibition following spontaneous K-complexes. We used time-frequency analysis of spontaneous K-complexes selected from whole-night EEG recordings of normal subjects. Our results show that spindles are most often observed at the positive phase following the peak of a spontaneous KC (70%). At latencies of 1-3 s following the peak of the K-complex, spindles almost disappear. Compared to long-term effects described for evoked KCs, sleep spindle power is not affected by spontaneous KCs for latencies of 5-15 s. Observation of the recurrence rate of sporadic spindles suggests that the reduction of power at 1-3 s most likely reflects a refractory period of spindles lasting for 1-2 s, rather than an effect of KCs. These results suggest that the mechanisms underlying spontaneous KCs do not affect spindle power as in the case of evoked KCs.

A single-trial toolbox for advanced sleep polysomnographic preprocessing

The application of polysomnographic (PSG) studies for monitoring sleep activity is a multi-parametric practice that involves a diverse group of biological signals. A suitable preprocessing of such signals assures a more profitable feature extraction and classification operations. Therefore, the proposed preprocessing toolbox performs segmentation, filtering, denoising, whitening and artefact removal tasks upon multi-channel PSG recordings. In order to assess toolbox's efficiency, clinical experiments are conducted, as well as, quantitative and qualitative metrics are discussed. Our findings reveal outperforming efficiency by artefacts and noise rejection after single-trial and multi-stage preprocessing.

Automatic K-complexes detection in sleep EEG recordings using likelihood thresholds

In this paper, we present an automatic method for K-complexes detection based on features extraction and the use of fuzzy thresholds. The validity of our process was examined on the basis of two visual K-complexes scorings performed on 5 excerpts of 30 minutes. Results were investigated through all different sleep stages. The algorithm provides global true positive rates of 61.72% and 60.94%, respectively with scorer 1 and scorer 2. The false positive proportions (compared to the total number of visually scored K-complexes) are of 19.62% and 181.25%, while the false positive rates estimated on a one 1 second resolution are only of 0.53% and 1.53%. These results suggest that our approach is completely suitable since its performances are similar to those of the human scorers.

The hypnospectrogram: an EEG power spectrum based means to concurrently overview the macroscopic and microscopic architecture of human sleep

This study introduces a complementary tool for the description and evaluation of human sleep. The nocturnal sleep electroencephalographic (EEG) time-frequency analysis (TFA) plot (hypnospectrogram for short) is hereby proposed as a means to visualize both the macroscopic and the microscopic architecture of human sleep. It provides the ability to concurrently visually inspect the coarse sleep architecture, that is, the time-course of non-rapid eye movement (NREM) and REM stages, along with finer sleep elements such as slow and fast spindles, NREM delta distribution, REM alpha and beta, microarousals (MAs), and NREM cyclic alternating patterns (CAPs). Furthermore, the hypnospectrogram has the potential to provide visual quality of sleep (QoS) evaluation, as well as reveal the dominant rhythms and their transitions for every cerebral locus - as represented at the electrode space - during the night.

Sleep spindle evolution from infancy to adolescence

OBJECTIVE

To investigate the development of frontally recorded sleep spindles from infancy to adolescence to provide normative data for pediatric sleep medicine.

METHODS

Sleep spindle activity was investigated in 120 healthy subjects aged 3 months to 16 years in 12 age groups. At 2 a.m. (min 1:17 a.m., max 3:18 a.m.) 10 min of NREM 2 was checked. Spindles were visually scored in the electroencephalogram from F4/A1.

RESULTS

The age dependency of interspindle interval, length of spindle, and spindle density, was statistically significant (Kruskal-Wallis p<0.0001). There were U-shaped curves for spindle length, spindle density, and an inverted U-shaped curve for the interspindle interval. Results of the post hoc U-test p<0.05 (Bonferroni corrected, m=66): Spindle length was minimal at 1.7 up to 3.0 years. Spindle density (number of spindles) was minimal between the ages of 1.7 and 2.3 years, thereafter there was a high increase that reached a plateau at age 5 years and remained up to 16 years. Interspindle interval was maximal at 1.7 and 2.3 years.

CONCLUSIONS

Sleep spindle activity changes with maturation in terms of length and density.

SIGNIFICANCE

The establishment of age-related normative data of sleep spindle activity can improve identification of NREM 2 in infancy, childhood, and adolescence, and enable detection of delayed neural maturation and/or sleep instability.