Comparison of EMG power during sleep from the submental and frontalis muscles

Non-REM sleep with hypertonia in Parkinsonian Spectrum Disorders: A pilot investigation

INTRODUCTION

From an ongoing multicenter effort toward differentiation of Parkinsonian spectrum disorders (PSD) from other types of neurodegenerative disorders, the sleep biomarker non-rapid-eye-movement sleep with hypertonia (NRH) emerged.

METHODS

This study included in the PSD group patients with dementia with Lewy bodies/Parkinson disease dementia (DLB/PDD = 16), Parkinson disease (PD = 16), and progressive supranuclear palsy (PSP = 13). The non-PSD group included patients with Alzheimer disease dementia (AD = 24), mild cognitive impairment (MCI = 35), and a control group with normal cognition (CG = 61). In-home, multi-night Sleep Profiler studies were conducted in all participants. Automated algorithms detected NRH, characterized by elevated frontopolar electromyographic power. Between-group differences in NRH were evaluated using Logistic regression, Mann-Whitney U and Chi-squared tests.

RESULTS

NRH was greater in the PSD group compared to non-PSD (13.9 ± 11.0% vs. 3.1 ± 4.7%, P < 0.0001). The threshold NRH≥5% provided the optimal between-group differentiation (AUC = 0.78, P < 0.001). NRH was independently associated with the PSD group after controlling for age, sex, and SSRI/SNRI use (P < 0.0001). The frequencies of abnormal NRH by subgroup were PSP = 92%, DLB/PDD = 81%, PD = 56%, MCI = 26%, AD = 17%, and CG = 16%. The odds of abnormal NRH in each PSD subgroup ranged from 3.7 to 61.2 compared to each non-PSD subgroup. The night-to-night and test-retest intraclass correlations were excellent (0.78 and 0.84, both P < 0.0001).

CONCLUSIONS

In this pilot study, NRH appeared to be a novel candidate sleep biomarker for PSD-related neurodegeneration. Future studies in larger cohorts are needed to confirm these findings, understand the etiology of NRH magnitude/duration, and determine whether it is an independent prodromal marker for specific neurodegenerative pathologies.

Automatic sleep stages classification using multi-level fusion

Sleep efficiency is a factor that can determine a person's healthy life. Sleep efficiency can be calculated by analyzing the results of the sleep stage classification. There have been many studies to classify sleep stages automatically using multiple signals to improve the accuracy of the sleep stage classification. The fusion method is used to process multi-signal data. Fusion methods include data-level fusion, feature-level fusion, and decision-level fusion methods. We propose a multi-level fusion method to increase the accuracy of the sleep stage classification when using multi-signal data consisting of electroencephalography and electromyography signals. First, we used feature-level fusion to fuse the extracted features using a convolutional neural network for multi-signal data. Then, after obtaining each classified result using the fused feature data, the sleep stage was derived using a decision-level fusion method that fused classified results. We used public datasets, Sleep-EDF, to measure performance; we confirmed that the proposed multi-level fusion method yielded a higher accuracy of 87.2%, respectively, compared to single-level fusion method and more existing methods. The proposed multi-level fusion method showed the most improved performance in classifying N1 stage, where existing methods had the lowest performance.

Sources of Variation in the Spectral Slope of the Sleep EEG

The 1/f spectral slope of the electroencephalogram (EEG) estimated in the γ frequency range has been proposed as an arousal marker that differentiates wake, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Here, we sought to replicate and extend these findings in a large sample, providing a comprehensive characterization of how slope changes with age, sex, and its test-retest reliability as well as potential confounds that could affect the slope estimation. We used 10,255 whole-night polysomnograms (PSGs) from the National Sleep Research Resource (NSRR). All preprocessing steps were performed using an open-source Luna package and the spectral slope was estimated by fitting log-log linear regression models on the absolute power from 30 to 45 Hz separately for wake, NREM, and REM stages. We confirmed that the mean spectral slope grows steeper going from wake to NREM to REM sleep. We found that the choice of mastoid referencing scheme modulated the extent to which electromyogenic, or electrocardiographic artifacts were likely to bias 30- to 45-Hz slope estimates, as well as other sources of technical, device-specific bias. Nonetheless, within individuals, slope estimates were relatively stable over time. Both cross-sectionally and longitudinal, slopes tended to become shallower with increasing age, particularly for REM sleep; males tended to show flatter slopes than females across all states. Our findings support that spectral slope can be a valuable arousal marker for both clinical and research endeavors but also underscore the importance of considering interindividual variation and multiple methodological aspects related to its estimation.

Evoking stress reactivity in virtual reality: A systematic review and meta-analysis

BACKGROUND

Virtual reality (VR) research probes stress environments that are infeasible to create in the real world. However, because research simulations are applied to narrow populations, it remains unclear if VR simulations can stimulate a broadly applicable stress-response. This systematic review and meta-analysis was conducted on studies using VR stress tasks and biomarkers.

METHODS

Included papers (N = 52) measured cortisol, heart rate (HR), galvanic skin response (GSR), systolic blood pressure (SBP), diastolic blood pressure (DBP), respiratory sinus arrhythmia (RSA), parasympathetic activity (RMSSD), sympathovagal balance (LF/HF), and/or salivary alpha-amylase (sAA). Effect sizes (ES) and confidence intervals (CI) were calculated based on standardized mean change of baseline-to-peak biomarker levels.

RESULTS

From baseline-to-peak (ES, CI), analyses showed a statistically significant change in cortisol (0.56, 0.28-0.83), HR (0.68, 0.53-0.82), GSR (0.59, 0.36-0.82), SBP (.55, 0.19-0.90), DBP (.64, 0.23-1.05), RSA (-0.59, -0.88 to -0.30), and sAA (0.27, 0.092-0.45). There was no effect for RMSSD and LF/HF.

CONCLUSION

VR stress tasks elicited a varied magnitude of physiological stress reactivity. VR may be an effective tool in stress research.

Clinical outcome measures in dementia with Lewy bodies trials: critique and recommendations

The selection of appropriate outcome measures is fundamental to the design of any successful clinical trial. Although dementia with Lewy bodies (DLB) is one of the most common neurodegenerative conditions, assessment of therapeutic benefit in clinical trials often relies on tools developed for other conditions, such as Alzheimer's or Parkinson's disease. These may not be sufficiently valid or sensitive to treatment changes in DLB, decreasing their utility. In this review, we discuss the limitations and strengths of selected available tools used to measure DLB-associated outcomes in clinical trials and highlight the potential roles for more specific objective measures. We emphasize that the existing outcome measures require validation in the DLB population and that DLB-specific outcomes need to be developed. Finally, we highlight how the selection of outcome measures may vary between symptomatic and disease-modifying therapy trials.

Detecting lucid dreams only by submentalis electromyography

Lucid dreams (LDs) occur when people become aware that they are dreaming. This phenomenon has a wide range of possible applications from the perspectives of psychology, training physical movements, and controlling computers while asleep, among others. However, research on LDs might lack efficiency because the standard LD verification protocol uses polysomnography (PSG), which requires an expensive apparatus and skilled staff. The standard protocol also may reduce LD-induction efficiency. The current study examines whether humans can send phasic signals through submentalis electromyography (EMG) during muscle atonia via pre-agreed chin movements (PACM). This ability would manifest both REM sleep and consciousness, which are the main features of LDs. In laboratory conditions volunteers were instructed to open their jaws three times while in an LD right after the standard verification protocol to achieve the research goal. Results: 4 of 5 volunteers proved to be in an LD using the standard protocol, and then all of them made PACM. The outcomes show that dream signals cannot be blocked in the submentalis area during muscle atonia. Also, this finding can be considered to develop a simplified, reliable LD protocol that needs only one EMG sensor. The cost of this protocol could be only a small percentage of the current protocol, making it more convenient for researchers and volunteers. It can also be used remotely by inbuilt in wearable gadgets. Considering PACM could speed up LD research and provide many discoveries and new opportunities. Also, it can be used in sleep paralysis studies.

Detection of microsleep states from the EEG: a comparison of feature reduction methods

Microsleeps are brief lapses in consciousness with complete suspension of performance. They are the cause of fatal accidents in many transport sectors requiring sustained attention, especially driving. A microsleep-warning device, using wireless EEG electrodes, could be used to rouse a user from an imminent microsleep. High-dimensional datasets, especially in EEG-based classification, present challenges as there are often a large number of potentially useful features for detecting the phenomenon of interest. Thus, it is often important to reduce the dimension of the original data prior to training the classifier. In this study, linear dimensionality reduction methods-principal component analysis (PCA) and probabilistic PCA (PPCA)-were compared with eight non-linear dimensionality reduction methods (kernel PCA, classical multi-dimensional scaling, isometric mapping, nearest neighbour estimation, stochastic neighbourhood embedding, autoencoder, stochastic proximity embedding, and Laplacian eigenmaps) on previously collected behavioural and EEG data from eight healthy non-sleep-deprived volunteers performing a 1D-visuomotor tracking task for 1 h. The effectiveness of the feature reduction algorithms was evaluated by visual inspection of class separation on 3D scatterplots, by trustworthiness scores, and by microsleep detection performance on a stacked-generalisation-based linear discriminant analysis (LDA) system estimating the microsleep/responsive state at 1 Hz based on the reduced features. On trustworthiness, PPCA outperformed PCA, but PCA outperformed all of the non-linear techniques. The trustworthiness score for each feature reduction method also correlated strongly with microsleep-state detection performance, providing strong validation of the ability of trustworthiness to estimate the relative effectiveness of feature reduction approaches, in terms of predicting performance, and ability to do so independently of the gold standard. Graphical abstract Proposed microsleep detection system.

Clinical trials in REM sleep behavioural disorder: challenges and opportunities

The rapid eye movement sleep behavioural disorder (RBD) population is an ideal study population for testing disease-modifying treatments for synucleinopathies, since RBD represents an early prodromal stage of synucleinopathy when neuropathology may be more responsive to treatment. While clonazepam and melatonin are most commonly used as symptomatic treatments for RBD, clinical trials of symptomatic treatments are also needed to identify evidence-based treatments. A comprehensive framework for both disease-modifying and symptomatic treatment trials in RBD is described, including potential treatments in the pipeline, cost-effective participant recruitment and selection, study design, outcomes and dissemination of results. For disease-modifying treatment clinical trials, the recommended primary outcome is phenoconversion to an overt synucleinopathy, and stratification features should be used to select a study population at high risk of phenoconversion, to enable more rapid clinical trials. For symptomatic treatment clinical trials, objective polysomnogram-based measurement of RBD-related movements and vocalisations should be the primary outcome measure, rather than subjective scales or diaries. Mobile technology to enable objective measurement of RBD episodes in the ambulatory setting, and advances in imaging, biofluid, tissue, and neurophysiological biomarkers of synucleinopathies, will enable more efficient clinical trials but are still in development. Increasing awareness of RBD among the general public and medical community coupled with timely diagnosis of these diseases will facilitate progress in the development of therapeutics for RBD and associated neurodegenerative disorders.

Save Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stages

Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.