Sleep stages classification using cardio-respiratory variables

The objective of the present study was to evaluate patterns of implicit processing in a task where the acquisition of explicit and implicit knowledge occurs simultaneously. The number reduction task (NRT) was used as having two levels of organization, overt and covert, where the covert level of processing is associated with implicit associative and implicit procedural learning. One aim was to compare these two types of implicit processes in the NRT when sleep was or was not introduced between initial formation of task representations and subsequent NRT processing. To assess the effects of different sleep stages, two sleep groups (early- and late-night groups) were used where initial training of the task was separated from subsequent retest by 3 h full of predominantly slow wave sleep (SWS) or rapid eye movement (REM) sleep. In two no-sleep groups, no interval was introduced between initial and subsequent NRT performance. A second aim was to evaluate the interaction between procedural and associative implicit learning in the NRT. Implicit associative learning was measured by the difference between the speed of responses that could or could not be predicted by the covert abstract regularity of the task. Implicit procedural on-line learning was measured by the practice-based increased speed of performance with time on task. Major results indicated that late-night sleep produced a substantial facilitation of implicit associations without modifying individual ability for explicit knowledge generation or for procedural on-line learning. This was evidenced by the higher rate of subjects who gained implicit knowledge of abstract task structure in the late-night group relative to the early-night and no-sleep groups. Independently of sleep, gain of implicit associative knowledge was accompanied by a relative slowing of responses to unpredictable items suggesting reciprocal interactions between associative and motor procedural processes within the implicit system. These observations provide evidence for the separability and interactions of different patterns of processing within implicit memory.

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Sleep Stages as Bifurcations at the Edge of Chaos

PsycEXTRA Dataset ◽

10.1037/e629492011-002 ◽

2011 ◽

Author(s):

Gaetano L. Aiello

Keyword(s):

Sleep Stages ◽

Edge Of Chaos

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Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

Applied Sciences ◽

10.3390/app10051797 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1797 ◽

Cited By ~ 2

Author(s):

Mera Kartika Delimayanti ◽

Bedy Purnama ◽

Ngoc Giang Nguyen ◽

Mohammad Reza Faisal ◽

Kunti Robiatul Mahmudah ◽

...

Keyword(s):

Machine Learning ◽

Sleep Stage ◽

Machine Learning Algorithms ◽

High Dimensional ◽

Sleep Stages ◽

Eeg Signals ◽

Stage Classification ◽

Sleep Stage Classification ◽

Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

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Capturing the Development of Internal Representations in a High-Performing Deep Network for Sleep Stage Classification

SN Computer Science ◽

10.1007/s42979-021-00697-3 ◽

2021 ◽

Vol 2 (4) ◽

Author(s):

Sarun Paisarnsrisomsuk ◽

Carolina Ruiz ◽

Sergio A. Alvarez

Keyword(s):

Input Data ◽

Single Channel ◽

Sleep Stage ◽

Quantitative Measure ◽

Classification Performance ◽

Sleep Stages ◽

Layer Depth ◽

Human Sleep ◽

Stage Classification ◽

Sleep Stage Classification

AbstractDeep neural networks can provide accurate automated classification of human sleep signals into sleep stages that enables more effective diagnosis and treatment of sleep disorders. We develop a deep convolutional neural network (CNN) that attains state-of-the-art sleep stage classification performance on input data consisting of human sleep EEG and EOG signals. Nested cross-validation is used for optimal model selection and reliable estimation of out-of-sample classification performance. The resulting network attains a classification accuracy of $$84.50 \pm 0.13\%$$ 84.50 ± 0.13 % ; its performance exceeds human expert inter-scorer agreement, even on single-channel EEG input data, therefore providing more objective and consistent labeling than human experts demonstrate as a group. We focus on analyzing the learned internal data representations of our network, with the aim of understanding the development of class differentiation ability across the layers of processing units, as a function of layer depth. We approach this problem visually, using t-Stochastic Neighbor Embedding (t-SNE), and propose a pooling variant of Centered Kernel Alignment (CKA) that provides an objective quantitative measure of the development of sleep stage specialization and differentiation with layer depth. The results reveal a monotonic progression of both of these sleep stage modeling abilities as layer depth increases.

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EEG microstate in obstructive sleep apnea patients

Scientific Reports ◽

10.1038/s41598-021-95749-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xin Xiong ◽

Yuyan Ren ◽

Shenghan Gao ◽

Jianhua Luo ◽

Jiangli Liao ◽

...

Keyword(s):

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Correlation Analysis ◽

Transition Probability ◽

Transient State ◽

Sample Entropy ◽

Sleep Stages ◽

Fluctuation Analysis ◽

Alpha Power ◽

Obstructive Sleep

AbstractObstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex.

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A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Sensors ◽

10.3390/s21051562 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1562

Author(s):

Syed Anas Imtiaz

Keyword(s):

Systematic Review ◽

Classification Accuracy ◽

Sleep Stages ◽

Sleep Staging ◽

Regulatory Requirements ◽

Wearable System ◽

Wearable Systems

Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

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Analysis and visualization of sleep stages based on deep neural networks

Neurobiology of Sleep and Circadian Rhythms ◽

10.1016/j.nbscr.2021.100064 ◽

2021 ◽

Vol 10 ◽

pp. 100064

Author(s):

Patrick Krauss ◽

Claus Metzner ◽

Nidhi Joshi ◽

Holger Schulze ◽

Maximilian Traxdorf ◽

...

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Sleep Stages

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Cerebral functional networks during sleep in young and older individuals

Scientific Reports ◽

10.1038/s41598-021-84417-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Véronique Daneault ◽

Pierre Orban ◽

Nicolas Martin ◽

Christian Dansereau ◽

Jonathan Godbout ◽

...

Keyword(s):

Functional Connectivity ◽

Brain Plasticity ◽

Network Connectivity ◽

Sleep Stages ◽

Functional Networks ◽

Older Individuals ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Age Related ◽

Light Sleep

AbstractEven though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.

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214 Longitudinal Changes in Sleep Duration, Timing, Variability, and Stages during the COVID-19 Pandemic: Large-Scale Fitbit Data

SLEEP ◽

10.1093/sleep/zsab072.213 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A86-A86

Author(s):

Michael Grandner ◽

Naghmeh Rezaei

Keyword(s):

New York ◽

Los Angeles ◽

San Francisco ◽

Sleep Duration ◽

Large Scale ◽

Age Groups ◽

Population Level ◽

Sleep Stages ◽

Longitudinal Changes ◽

Timing Variability

Abstract Introduction The COVID-19 pandemic has resulted in societal-level changes to sleep and other behavioral patterns. Objective, longitudinal data would allow for a greater understanding of sleep-related changes at the population level. Methods N= 163,524 deidentified active Fitbit users from 6 major US cities contributed data, representing areas particularly hard-hit by the pandemic (Chicago, Houston, Los Angeles, New York, San Francisco, and Miami). Sleep variables extracted include nightly and weekly mean sleep duration and bedtime, variability (standard deviation) of sleep duration and bedtime, and estimated arousals and sleep stages. Deviation from similar timeframes in 2019 were examined. All analyses were performed in Python. Results These data detail how sleep duration and timing changed longitudinally, stratified by age group and gender, relative to previous years’ data. Overall, 2020 represented a significant departure for all age groups and both men and women (P<0.00001). Mean sleep duration increased in nearly all groups (P<0.00001) by 5-11 minutes, compared to a mean decrease of 5-8 minutes seen over the same period in 2019. Categorically, sleep duration increased for some and decreased for others, but more extended than restricted. Sleep phase shifted later for nearly all groups (p<0.00001). Categorically, bedtime was delayed for some and advanced for others, though more delayed than advanced. Duration and bedtime variability decreased, owing largely to decreased weekday-weekend differences. WASO increased, REM% increased, and Deep% decreased. Additional analyses show stratified, longitudinal changes to sleep duration and timing mean and variability distributions by month, as well as effect sizes and correlations to other outcomes. Conclusion The pandemic was associated with increased sleep duration on average, in contrast to 2019 when sleep decreased. The increase was most profound among younger adults, especially women. The youngest adults also experienced the greatest bedtime delay, in line with extensive school-start-times and chronotype data. When given the opportunity, the difference between weekdays and weekends became smaller, with occupational implications. Sleep staging data showed that slightly extending sleep minimally impacted deep sleep but resulted in a proportional increase in REM. Wakefulness during the night also increased, suggesting increased arousal despite greater sleep duration. Support (if any) This research was supported by Fitbit, Inc.

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