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On the basis of polysomnographic laboratory and field studies, the DLR Institute of Aerospace Medicine has developed a concept to protect against adverse effects of nocturnal aircraft noise at Airport Leipzig/Halle, which will be extended to a freight hub. We investigated whether or not the expected high traffic densities duri...
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... amount of delta waves showing amplitudes of at least 75 lV and a frequency of 2 Hz or less, the so-called slow waves, is the main criterion for scoring sleep stages 3 (20-50 % slow waves) and 4 (at least 50 % slow waves) according to R&K. As R&K suggested, slow waves should be measured 'wave by wave' ( figure A3). ...
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The scientific discussion of sleep spindles now routinely mentions the association between these oscillations and cognitive ability. Numerous studies have been published on the topic with various methodologies and highly divergent results. In this meta-analysis of the relevant literature (total k = 22 articles, total N = 953 subjects), it is conclu...
A new technique for nonparametric regression of multichannel signals is presented. The technique is based on the use of the Rational-Dilation Wavelet Transform (RADWT), equipped with a tunable Q-factor able to provide sparse representations of functions with different oscillations persistence. In particular , two different frames are obtained by tw...
The correlation method from brain imaging has been used to estimate functional connectivity in the human brain. However, brain regions might show very high correlation even when the two regions are not directly connected due to the strong interaction of the two regions with common input from a third region. One previously proposed solution to this...
In recent years, spindle-shaped electrical activity became interesting for researchers looking for new methods of time-frequency analysis of electromyograms (EMG) and acceleration (ACC) signals. We call signals of this type as wave trains; a wave train (a wave packet) is an electrical signal that is localized in space, frequency, and time. Examples...
Citations
... Amplitudes between 1 and 100 µV are considered to be 'normal' while certain other patterns of either healthy or pathological origin (e.g. bursts (during burst-suppression), encephalopathy, K-complex, hypsarrhythmia) can reach amplitudes of over 100-200 µV (Teplan 2002, Rodenbeck et al 2006, Mytinger et al 2015, Hirsch et al 2021. A patient with a high EEG amplitude (e.g. 100 µV) is more likely to suffer from cropping than a patient with a low EEG amplitude (e.g. 25 µV) when the display settings are the same. ...
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 values cropping or high digitization error. We aimed to systematically assess the impact of those distortions on metrics used for clinical research in the field of neuromonitoring.
Approach: A 16h 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 total power, alpha delta ratio, and 95% spectral edge frequency. Data were analyzed by creating violin-plots or box-plots.
Main Results: Cropping led to a continuous reduction in total and band power leading to corresponding changes in variability thereof. Relative power and alpha delta ratio were less affected. Changes in resolution led to relevant changes. While total power and power of low frequencies were rather stable, 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. In order 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.
... The EEG signals are collected in a non-invasive way that involves placing electrodes on the scalp to measure and record the electrical impulses generated by the brain [130]. Due to the characteristic that the EEG signals are the external representation of the inner brain neural activity, which contains abundant neural information related to various brain stimuli, EEG signals have been widely studied to deal with different real-world tasks: for example, epilepsy recognition [50], emotion recognition [18], sleep research [114], and the brain-computer interface application [3]. Therefore, the EEG signal is an incredible tool in neuroscience and possesses an exceptionally high clinical utility, which generally became the research focus of physiological signals. ...
Electroencephalogram (EEG) is a non-invasive technique to record bioelectrical signals. Integrating supervised deep learning techniques with EEG signals has recently facilitated automatic analysis across diverse EEG-based tasks. However, the label issues of EEG signals have constrained the development of EEG-based deep models. Obtaining EEG annotations is difficult that requires domain experts to guide collection and labeling, and the variability of EEG signals among different subjects causes significant label shifts.
To solve the above challenges, self-supervised learning (SSL) has been proposed to extract representations from unlabeled samples through well-designed pretext tasks. This paper concentrates on integrating SSL frameworks with temporal EEG signals to achieve efficient representation and proposes a systematic review of the SSL for EEG signals. In this paper, 1) we introduce the concept and theory of self-supervised learning and typical SSL frameworks. 2) We provide a comprehensive review of SSL for EEG analysis, including taxonomy, methodology, and technique details of the existing EEG-based SSL frameworks, and discuss the difference between these methods. 3) We investigate the adaptation of the SSL approach to various downstream tasks, including the task description and related benchmark datasets. 4) Finally, we discuss the potential directions for future SSL-EEG research.
... The recorded EEG signals are classified by varying proportions of spectral bands of electrical activity from the brain. Typical EEG bands and their respective frequency boundaries [gamma , beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), alpha (8)(9)(10)(11)(12)(13), theta (4-7 Hz), and delta (0.1 < 4 Hz)] allow researchers to characterize detailed sleep architecture (16,17). ...
... N2 is proceeded by the final stage of NREM sleep: stage 3 (N3), also called slow-wave sleep (SWS) (note that this stage was previously subdivided into NREM stages 3 and 4). N3 is characterized by a predominance of delta power, which results from high synchronicity in cortical pyramidal neurons, driven by progressive hyperpolarization of thalamocortical loops (18). In healthy adult sleep, subjects return to N2 before entering rapid eye movement (REM) sleep. ...
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.
Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 64 is January 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... We also performed cross-frequency analyses of the LFP signals detected in the ACC and LH, which showed distinct LFP coupling features between LFP-Group 1 and 2 (Fig. 2b). Of note, this spectral-based differentiation of brain dynamic stages is in agreement with sleep EEG studies showing that during NREM sleep, i.e. the deepest state of unconsciousness, the cortical activity peaks ∼2 Hz (Benoit et al. 2000;Rodenbeck et al. 2006;Olbrich et al. 2017). In contrast, during REM sleep, i.e. a state of relative awareness, the frequency peak of the EEG shifts to higher values (e.g. ...
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.
... Before running the independent component analysis for the correction of eye-movement artefacts, the EEG data were downsampled to 500 Hz. Lastly, the data were visually screened and sleep elements (sleep-spindles and K-complexes, as defined by Rodenbeck et al. (2006)) were marked. ...
... They usually precede or accompany the onset of REM sleep; often increase in density before/during REM may reflect the transition between tonic and phasic REM sleep and are more frequent late at night. [42][43][44][45][46] Little is known about the mechanisms by which they are generated and their functional role. Their occurrence has been associated with an in-phase increase in fast-frequency activity, ...
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.
... Sleep stages and EEG wave type characteristics(Al-Salman et al., 2019;Rodenbeck et al., 2006). ...
As interest in circadian rhythms and their effects continues to grow, there is an increasing need to perform circadian studies in humans. Although the constant
routine is the gold standard for these studies, there are advantages to performing more naturalistic studies. Here, a review of protocols for such studies is
provided along with sample inclusion and exclusion criteria. Sleep routines, drug use, shift work, and menstrual cycle are addressed as screening considerations.
Regarding protocol, best practices for measuring melatonin, including light settings, posture, exercise, and dietary habits are described. The inclusion/exclusion
recommendations and protocol guidelines are intended to reduce confounding variables in studies that do not involve the constant routine. Given practical
limitations, a range of recommendations is provided from stringent to lenient. The scientific rationale behind these recommendations is discussed. However,
where the science is equivocal, recommendations are based on empirical decisions made in previous studies. While not all of the recommendations listed may be
practical in all research settings and with limited potential participants, the goal is to allow investigators to make well-informed decisions about their screening
procedures and protocol techniques and to improve rigor and reproducibility, in line with the objectives of the National Institutes of Health.
... The raw EEG signals were first filtered with a 50 Hz notch filter to remove powerline interference, and the DC offset was removed from each channel. In order to increase the precision of the start time and duration of the detected sleep spindles, the pre-processed EEG signal recorded on channel F4-C4 and F3-C3 was segmented into 0.5 s epochs with 0.25 s overlap, as the minimum required length of a sleep spindle is 0.5 s [44]. The Deep-spindle architecture, which employs a CNN combined with a bidirectional LSTM network is presented in Fig. 2. ...
... Post-processing: Rodenbeck et al. [44] proposed that the length of 0.5 s is the minimum required length of a sleep spindle [44]. Therefore, if the duration of a predicted sleep spindle was not greater than 0.5 s, the event was re-labelled as a non-sleep spindle event. ...
... Post-processing: Rodenbeck et al. [44] proposed that the length of 0.5 s is the minimum required length of a sleep spindle [44]. Therefore, if the duration of a predicted sleep spindle was not greater than 0.5 s, the event was re-labelled as a non-sleep spindle event. ...
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.
... Among these, EEG is mostly used by sleep specialists to diagnose sleep-related problems. After overnight recording, these biosignals are interpreted by the sleep specialist doctors, and scoring is completed following the Rechtschaffen and Kales (R & K) rules, which were identified in 1968 and later modified by the American Academy of Sleep Medicine (AASM) [2]. Based on this scoring, sleep is divided into different stages named wakeness (W), non-rapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. ...
An automated sleep stage categorization can readily face noise-contaminated EEG record-ings, just as other signal processing applications. Therefore, the denoising of the contaminated signalsis inevitable to ensure a reliable analysis of the EEG signals. In this research work, an empirical modedecomposition is used in combination with stacked autoencoders to conduct automatic sleep stageclassification with reliable analytical performance. Due to the decomposition of the composite signalinto several intrinsic mode functions, empirical mode decomposition offers an effective solutionfor denoising non-stationary signals such as EEG. Preliminary results showed that through theseintrinsic modes, a signal with a high signal-to-noise ratio can be obtained, which can be used forfurther analysis with confidence. Therefore, later, when statistical features were extracted from thedenoised signals and were classified using stacked autoencoders, improved results were obtained forStage 1, Stage 2, Stage 3, Stage 4, and REM stage EEG signals using this combination.
... In 1953, Nathaniel Kleitman and Aserinsky discovered the characteristics of eye movement during sleep, divide them to rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep (Jean-Baptiste et al., 2014). In 1968, Allan Rechtschaffen and Anthony Kales divided NREM into four stages using the known R&K rules (Rodenbeck et al., 2010). In 2007, American Academy of Sleep Medicine reformulated a new classification manual for sleep classification called AASM rules, combining the NREM sleep stage 3 (N3) and NREM sleep stage 4 (N4) in the R&K standard (Choi et al., 2010). ...
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.
