Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Save Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stages.

ABSTRACT: 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.

SUBMITTER: Liu GR

PROVIDER: S-EPMC7180982 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Save Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stages.

Liu Gi-Ren GR Lustenberger Caroline C Lo Yu-Lun YL Liu Wen-Te WT Sheu Yuan-Chung YC Wu Hau-Tieng HT

Sensors (Basel, Switzerland) 20200403 7

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 a ...[more]

PMID: 32260314

Similar Datasets

Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages.

Project description:In clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a super-statistical analysis by computing hyper-parameters, such as the standard deviation, kurtosis, and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than sub-divided process that controls human sleep. Based on the hyper-parameters, we finally perform a pairwise similarity analysis between the different sleep stages, using a quantitative measure for the separability of data clusters in multi-dimensional spaces.

| S-EPMC8664947 | biostudies-literature

EEG Signal Diversity Varies With Sleep Stage and Aspects of Dream Experience.

Project description:Several theories link consciousness to complex cortical dynamics, as suggested by comparison of brain signal diversity between conscious states and states where consciousness is lost or reduced. In particular, Lempel-Ziv complexity, amplitude coalition entropy and synchrony coalition entropy distinguish wakefulness and REM sleep from deep sleep and anesthesia, and are elevated in psychedelic states, reported to increase the range and vividness of conscious contents. Some studies have even found correlations between complexity measures and facets of self-reported experience. As suggested by integrated information theory and the entropic brain hypothesis, measures of differentiation and signal diversity may therefore be measurable correlates of consciousness and phenomenological richness. Inspired by these ideas, we tested three hypotheses about EEG signal diversity related to sleep and dreaming. First, diversity should decrease with successively deeper stages of non-REM sleep. Second, signal diversity within the same sleep stage should be higher for periods of dreaming vs. non-dreaming. Third, specific aspects of dream contents should correlate with signal diversity in corresponding cortical regions. We employed a repeated awakening paradigm in sleep deprived healthy volunteers, with immediate dream report and rating of dream content along a thought-perceptual axis, from exclusively thought-like to exclusively perceptual. Generalized linear mixed models were used to assess how signal diversity varied with sleep stage, dreaming and thought-perceptual rating. Signal diversity decreased with sleep depth, but was not significantly different between dreaming and non-dreaming, even though there was a significant positive correlation between Lempel-Ziv complexity of EEG recorded over the posterior cortex and thought-perceptual ratings of dream contents.

| S-EPMC8102678 | biostudies-literature

Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data.

Project description:PurposeTo develop an automated framework for sleep stage scoring from PSG via a deep neural network.MethodsAn automated deep neural network was proposed by using a multi-model integration strategy with multiple signal channels as input. All of the data were collected from one single medical center from July 2017 to April 2019. Model performance was evaluated by overall classification accuracy, precision, recall, weighted F1 score, and Cohen's Kappa.ResultsTwo hundred ninety-four sleep studies were included in this study; 122 composed the training dataset, 20 composed the validation dataset, and 152 were used in the testing dataset. The network achieved human-level annotation performance with an average accuracy of 0.8181, weighted F1 score of 0.8150, and Cohen's Kappa of 0.7276. Top-2 accuracy (the proportion of test samples for which the true label is among the two most probable labels given by the model) was significantly improved compared to the overall classification accuracy, with the average being 0.9602. The number of arousals affected the model's performance.ConclusionThis research provides a robust and reliable model with the inter-rater agreement nearing that of human experts. Determining the most appropriate evaluation parameters for sleep staging is a direction for future research.

| S-EPMC7289784 | biostudies-literature

Alloy information helps prioritize material criticality lists.

Project description:Materials scientists employ metals and alloys that involve most of the periodic table. Nonetheless, materials scientists rarely take material criticality and reuse potential into account. In this work, we expand upon lists of "critical materials" generated by national and regional governments by showing that many materials are employed predominantly as alloying elements, which can be a deterrent to recovery and reuse at end of product life and, likely as a consequence, have low functional end-of-life recycling rates, among other problematic characteristics. We thereby single out six metals for enhanced concern: dysprosium, samarium, vanadium, niobium, tellurium, and gallium. From that perspective, the use of critical metals in low concentrations in alloys unlikely to be routinely recycled should be avoided if possible. If not, provision should be made for better identification and more efficient recycling so that materials designated as critical can have increased potential for more than a single functional use.

| S-EPMC8748655 | biostudies-literature

Vagus Nerve Ultrasonography Helps Distinguish Multiple System Atrophy from Other Parkinsonian Syndromes.

Project description:BackgroundDistinguishing multiple system atrophy from other parkinsonian syndromes is challenging.ObjectivesTo evaluate vagus nerve ultrasonography for differentiating parkinsonian syndromes.MethodsA single-center, cross-sectional, observational study assessed 85 consecutive adult patients with de novo parkinsonism between June 2020 and December 2022, using 12 MHz ultrasonography of the vagus nerve cross-sectional area.ResultsBilateral vagus nerves were smaller in multiple system atrophy than in other parkinsonian syndromes. The area under the receiver operating characteristic curve for differentiating multiple system atrophy was 0.79 on the right side and 0.74 on the left. The cut-off values to diagnose multiple system atrophy were 0.71 and 0.86 mm2 on the right and left sides, respectively, with sensitivities of 82.6% and 87.0%, specificities of 74.2% and 64.5%, positive predictive values of 54% and 47.6%, and negative predictive values of 92.0% and 93.0%.ConclusionsVagus nerve ultrasonography may differentiate multiple system atrophy from other parkinsonian syndromes.

| S-EPMC10585973 | biostudies-literature

High Variability Periods in the EEG Distinguish Cognitive Brain States

Project description: Not available

| S-EPMC10669877 | biostudies-literature

Identifying signal-dependent information about the preictal state: A comparison across ECoG, EEG and EKG using deep learning.

Project description:BackgroundThe inability to reliably assess seizure risk is a major burden for epilepsy patients and prevents developing better treatments. Recent advances have paved the way for increasingly accurate seizure preictal state detection algorithms, primarily using electrocorticography (ECoG). To develop seizure forecasting for broad clinical and ambulatory use, however, less complex and invasive modalities are needed. Algorithms using scalp electroencephalography (EEG) and electrocardiography (EKG) have also achieved better than chance performance. But it remains unknown how much preictal information is in ECoG versus modalities amenable to everyday use - such as EKG and single channel EEG - and how to optimally extract that preictal information for seizure prediction.MethodsWe apply deep learning - a powerful method to extract information from complex data - on a large epilepsy data set containing multi-day, simultaneous recordings of EKG, ECoG, and EEG, using a variety of feature sets. We use the relative performance of our algorithms to compare the preictal information contained in each modality.ResultsWe find that single-channel EKG contains a comparable amount of preictal information as scalp EEG with up to 21 channels and that preictal information is best extracted not with standard heart rate measures, but from the power spectral density. We report that preictal information is not preferentially contained in EEG or ECoG channels within the seizure onset zone.ConclusionCollectively, these insights may help to devise future prospective, minimally invasive long-term epilepsy monitoring trials with single-channel EKG as a particularly promising modality.

| S-EPMC6642360 | biostudies-literature

The sleep apnea cardiovascular endpoints (SAVE) trial: Rationale and start-up phase.

Project description:THE SLEEP APNEA CARDIOVASCULAR ENDPOINTS (SAVE) STUDY (CLINICAL TRIALS REGISTRATION NUMBER: NCT00738170) is an academic initiated and conducted, multinational, open, blinded endpoint, randomised controlled trial designed to determine whether treatment of obstructive sleep apnea (OSA) with continuous positive airways pressure (CPAP) can reduce the incidence of serious cardiovascular events in patients with established cardiovascular disease. The answer to this question is of major importance to populations undergoing ageing and lifestyle changes all over the world. The SAVE study brings together respiratory, sleep and cardiovascular clinician-scientists in a unique interdisciplinary collaborative effort with industry sponsors to conduct the largest and most ambitious clinical trial yet conducted in the field of sleep apnea, with a global recruitment target of 5000 patients. Following its launch in Australia and China in late 2008, SAVE has now entered a phase of international expansion with new recruitment networks being established in New Zealand, India and Latin America. This article describes the rationale for the SAVE study, the considerations behind its design, and progress thus far in establishing the recruitment network. The report emphasises the important role that Chinese sleep and cardiovascular investigators have played in the start-up phase of this landmark international project.

| S-EPMC3256461 | biostudies-other

On the Individuality of Sleep EEG Spectra.

Project description:Research in recent years has supported the hypothesis that many properties of the electroencephalogram (EEG) are specific to an individual. In this study, the intra- and inter-individual variations of sleep EEG signals were investigated. This was carried out by analyzing the stability of the average EEG spectra individually computed for the Rechtschaffen and Kales (RK) sleep stages. Six EEG channels were used to account for the topographical aspect of the analysis. Validity of the results was supported by considering a wide dataset of 174 subjects with normal sleep. Subjects spent two consecutive nights in the sleep laboratory during which EEG recordings were obtained. High similarity between average spectra of two consecutive nights was found considering an individual. More than 89% of the second night recordings were correctly assigned to their counterparts of the first night. The average spectra of sleep EEG computed for each RK sleep stage have shown a high degree of individuality.

| S-EPMC3755818 | biostudies-other

Gene-expression signatures can distinguish gastric cancer grades and stages.

Project description:Microarray gene-expression data of 54 paired gastric cancer and adjacent noncancerous gastric tissues were analyzed, with the aim to establish gene signatures for cancer grades (well-, moderately-, poorly- or un-differentiated) and stages (I, II, III and IV), which have been determined by pathologists. Our statistical analysis led to the identification of a number of gene combinations whose expression patterns serve well as signatures of different grades and different stages of gastric cancer. A 19-gene signature was found to have discerning power between high- and low-grade gastric cancers in general, with overall classification accuracy at 79.6%. An expanded 198-gene panel allows the stratification of cancers into four grades and control, giving rise to an overall classification agreement of 74.2% between each grade designated by the pathologists and our prediction. Two signatures for cancer staging, consisting of 10 genes and 9 genes, respectively, provide high classification accuracies at 90.0% and 84.0%, among early-, advanced-stage cancer and control. Functional and pathway analyses on these signature genes reveal the significant relevance of the derived signatures to cancer grades and progression. To the best of our knowledge, this represents the first study on identification of genes whose expression patterns can serve as markers for cancer grades and stages.

| S-EPMC3060867 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data