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Sleep Medicine Feb 2022Sleep is paramount for optimal brain development in infants admitted to the neonatal intensive care unit. Besides (minimally) invasive technical approaches to study... (Review)
Review
BACKGROUND
Sleep is paramount for optimal brain development in infants admitted to the neonatal intensive care unit. Besides (minimally) invasive technical approaches to study sleep in infants, there is currently a large variety of behavioral sleep stage classification methods (BSSCs) that can be used to identify sleep stages in preterm infants born <37 weeks gestational age. However, they operate different criteria to define sleep stages, which limits the comparability and reproducibility of research on preterm sleep. This scoping review aims to: 1) identify and elaborate on existing neonatal BSSCs used for preterm infants, 2) examine the reliability and validity of these BSSCs, and 3) identify which criteria are most used for different ages, ranging from 23 to 37 weeks postmenstrual age at observation.
METHODS
To map the existing BSSCs, PubMed, EMBASE and Cochrane were searched for studies using a BSSC to identify sleep stages in preterm infants.
RESULTS
In total, 36 BSSCs were identified with on average five item categories assessed per BSSC, most frequently: eyes, body movements, facial movements, sounds, and respiratory pattern. Furthermore, validity and reliability of the BSSCs were tested in less than half of the included studies. Finally, BSSCs were used in infants of all ages, regardless the age for which the BSSC was originally developed.
CONCLUSIONS
Items used for scoring in the different BSSCs were relatively consistent. The age ranges, reliability, and validity of the BSSCs were not consistently reported in most studies. Either validation studies of existing BSSCs or new BSSCs are necessary to improve the comparability and reproducibility of previous and future preterm behavioral sleep studies.
Topics: Humans; Infant; Infant, Newborn; Infant, Premature; Intensive Care Units, Neonatal; Reproducibility of Results; Sleep; Sleep Stages
PubMed: 35123149
DOI: 10.1016/j.sleep.2022.01.006 -
Sensors (Basel, Switzerland) Mar 2023The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method...
The analysis of sleep stages for children plays an important role in early diagnosis and treatment. This paper introduces our sleep stage classification method addressing the following two challenges: the first is the data imbalance problem, i.e., the highly skewed class distribution with underrepresented minority classes. For this, a Gaussian Noise Data Augmentation (GNDA) algorithm was applied to polysomnography recordings to seek the balance of data sizes for different sleep stages. The second challenge is the difficulty in identifying a minority class of sleep stages, given their short sleep duration and similarities to other stages in terms of EEG characteristics. To overcome this, we developed a DeConvolution- and Self-Attention-based Model (DCSAM) which can inverse the feature map of a hidden layer to the input space to extract local features and extract the correlations between all possible pairs of features to distinguish sleep stages. The results on our dataset show that DCSAM based on GNDA obtains an accuracy of 90.26% and a macro F1-score of 86.51% which are higher than those of our previous method. We also tested DCSAM on a well-known public dataset-Sleep-EDFX-to prove whether it is applicable to sleep data from adults. It achieves a comparable performance to state-of-the-art methods, especially accuracies of 91.77%, 92.54%, 94.73%, and 95.30% for six-stage, five-stage, four-stage, and three-stage classification, respectively. These results imply that our DCSAM based on GNDA has a great potential to offer performance improvements in various medical domains by considering the data imbalance problems and correlations among features in time series data.
Topics: Adult; Humans; Child; Electroencephalography; Sleep; Sleep Stages; Polysomnography; Algorithms
PubMed: 37050506
DOI: 10.3390/s23073446 -
Current Biology : CB Oct 2016Sleep is characterized by unique patterns of cortical activity alternating between the stages of slow-wave sleep (SWS) and rapid-eye movement (REM) sleep. How these...
Sleep is characterized by unique patterns of cortical activity alternating between the stages of slow-wave sleep (SWS) and rapid-eye movement (REM) sleep. How these patterns relate to the balanced activity of excitatory pyramidal cells and inhibitory interneurons in cortical circuits is unknown. We investigated cortical network activity during wakefulness, SWS, and REM sleep globally and locally using in vivo calcium imaging in mice. Wide-field imaging revealed a reduction in pyramidal cell activity during SWS compared with wakefulness and, unexpectedly, a further profound reduction in activity during REM sleep. Two-photon imaging on local circuits showed that this suppression of activity during REM sleep was accompanied by activation of parvalbumin (PV)+ interneurons, but not of somatostatin (SOM)+ interneurons. PV+ interneurons most active during wakefulness were also most active during REM sleep. Our results reveal a sleep-stage-specific regulation of the cortical excitation/inhibition balance, with PV+ interneurons conveying maximum inhibition during REM sleep, which might help shape memories in these networks.
Topics: Animals; Interneurons; Male; Mice; Parvalbumins; Pyramidal Cells; Sleep Stages; Sleep, REM; Somatostatin; Wakefulness
PubMed: 27693142
DOI: 10.1016/j.cub.2016.08.035 -
Neuroscience Research May 2017According to a two-stage memory consolidation model, during waking theta states, afferent activity from the neocortex to the hippocampus induces transient synaptic... (Review)
Review
According to a two-stage memory consolidation model, during waking theta states, afferent activity from the neocortex to the hippocampus induces transient synaptic modification in the hippocampus, where the information is deposited as a labile form of memory trace. During subsequent sharp-wave ripples (SPW-Rs), the newly acquired hippocampal information is transferred to the neocortex and stored as a long-lasting memory trace. Consistent with this hypothesis, waking theta states and SPW-Rs distinctly control information flow in the hippocampal-entorhinal loop. Although both waking theta states and rapid eye movement (REM) sleep are characterized by prominent hippocampal theta oscillations, the two brain states involve distinct temporal coordination and oscillatory coupling in the hippocampal-entorhinal circuit. While distinct brain states have distinct network dynamics, firing rates of individual neurons in the hippocampal-entorhinal circuitry follow lognormal-like distributions in all states. Firing rates of the same neurons are positively correlated across brain states and testing environments, suggesting that memory is allocated in preconfigured, rather than tabula rasa-type, skewed neuronal networks. The fast-firing minority and slow-firing majority neurons, which can support network stability and flexibility, are under distinct homeostatic regulations that are initiated by spindles and SPW-Rs during slow wave sleep and implemented during subsequent REM sleep.
Topics: Animals; Hippocampus; Humans; Memory Consolidation; Sleep Stages; Wakefulness
PubMed: 28506629
DOI: 10.1016/j.neures.2017.04.018 -
Journal of Clinical Sleep Medicine :... Jan 2022We evaluated the interrater reliabilities of manual polysomnography sleep stage scoring. We included all studies that employed Rechtschaffen and Kales rules or American... (Meta-Analysis)
Meta-Analysis
STUDY OBJECTIVES
We evaluated the interrater reliabilities of manual polysomnography sleep stage scoring. We included all studies that employed Rechtschaffen and Kales rules or American Academy of Sleep Medicine standards. We sought the overall degree of agreement and those for each stage.
METHODS
The keywords were "Polysomnography (PSG)," "sleep staging," "Rechtschaffen and Kales (R&K)," "American Academy of Sleep Medicine (AASM)," "interrater (interscorer) reliability," and "Cohen's kappa." We searched PubMed, OVID Medline, EMBASE, the Cochrane library, KoreaMed, KISS, and the MedRIC. The exclusion criteria included automatic scoring and pediatric patients. We collected data on scorer histories, scoring rules, numbers of epochs scored, and the underlying diseases of the patients.
RESULTS
A total of 101 publications were retrieved; 11 satisfied the selection criteria. The Cohen's kappa for manual, overall sleep scoring was 0.76, indicating substantial agreement (95% confidence interval, 0.71-0.81; < .001). By sleep stage, the figures were 0.70, 0.24, 0.57, 0.57, and 0.69 for the W, N1, N2, N3, and R stages, respectively. The interrater reliabilities for stage N2 and N3 sleep were moderate, and that for stage N1 sleep was only fair.
CONCLUSIONS
We conducted a meta-analysis to generalize the variation in manual scoring of polysomnography and provide reference data for automatic sleep stage scoring systems. The reliability of manual scorers of polysomnography sleep stages was substantial. However, for certain stages, the results were poor; validity requires improvement.
CITATION
Lee YJ, Lee JY, Cho JH, Choi JH. Interrater reliability of sleep stage scoring: a meta-analysis. 2022;18(1):193-202.
Topics: Child; Humans; Observer Variation; Polysomnography; Reproducibility of Results; Sleep; Sleep Stages
PubMed: 34310277
DOI: 10.5664/jcsm.9538 -
Journal of Sleep Research Feb 2024Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually...
Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually following visual scoring rules there can be considerable variation in the sleep staging between different scorers. Thus, this study aimed to comprehensively evaluate the inter-rater agreement in sleep staging. A total of 50 polysomnography recordings were manually scored by 10 independent scorers from seven different sleep centres. We used the 10 scorings to calculate a majority score by taking the sleep stage that was the most scored stage for each epoch. The overall agreement for sleep staging was κ = 0.71 and the mean agreement with the majority score was 0.86. The scorers were in perfect agreement in 48% of all scored epochs. The agreement was highest in rapid eye movement sleep (κ = 0.86) and lowest in N1 sleep (κ = 0.41). The agreement with the majority scoring varied between the scorers from 81% to 91%, with large variations between the scorers in sleep stage-specific agreements. Scorers from the same sleep centres had the highest pairwise agreements at κ = 0.79, κ = 0.85, and κ = 0.78, while the lowest pairwise agreement between the scorers was κ = 0.58. We also found a moderate negative correlation between sleep staging agreement and the apnea-hypopnea index, as well as the rate of sleep stage transitions. In conclusion, although the overall agreement was high, several areas of low agreement were also found, mainly between non-rapid eye movement stages.
Topics: Humans; Observer Variation; Reproducibility of Results; Sleep; Sleep Stages; Sleep Apnea Syndromes
PubMed: 37309714
DOI: 10.1111/jsr.13956 -
International Journal of Environmental... Mar 2021Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained...
Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet's cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen's Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen's Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.
Topics: Electroencephalography; Humans; Polysomnography; Sleep; Sleep Stages; Sleep Wake Disorders
PubMed: 33802799
DOI: 10.3390/ijerph18063087 -
PloS One 2023Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep...
Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person's sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures-both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.
Topics: Male; Humans; Actigraphy; Artificial Intelligence; Heart Rate; Sleep; Sleep Stages; Time Factors; Reproducibility of Results
PubMed: 37195925
DOI: 10.1371/journal.pone.0285703 -
Frontiers in Public Health 2022Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the...
Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithms to extract feature information before applying it in the sleep stages. Conventional feature extraction methods have low efficiency and are difficult to meet the time validity of fast staging. In addition, it can easily lead to the omission of key features owing to insufficient a priori knowledge. Deep learning networks, such as convolutional neural networks (CNNs), have powerful processing capabilities in data analysis and data mining. In this study, a deep learning network is introduced into the study of the sleep stage. In this study, the feature fusion method is presented, and long-term and short-term memory (LSTM) is selected as the classification network to improve the accuracy of sleep stage recognition. First, based on EEG and deep learning network, an automatic sleep phase method based on a multi-channel EGG is proposed. Second, CNN-LSTM is used to monitor EEG and EOG samples during sleep. In addition, without any signal preprocessing or feature extraction, data expansion (DA) can be realized for unbalanced data, and special data and non-general data can be deleted. Finally, the MIT-BIH dataset is used to train and evaluate the proposed model. The experimental results show that the EEG-based sleep phase method proposed in this paper provides an effective method for the diagnosis and treatment of sleep disorders, and hence has a practical application value.
Topics: Electroencephalography; Humans; Signal Processing, Computer-Assisted; Sleep; Sleep Stages; Sleep Wake Disorders
PubMed: 35968483
DOI: 10.3389/fpubh.2022.946833 -
Scientific Reports Jul 2022Scoring sleep stages from biological signals is an essential but labor-intensive inspection for sleep diagnosis. The existing automated scoring methods have achieved...
Scoring sleep stages from biological signals is an essential but labor-intensive inspection for sleep diagnosis. The existing automated scoring methods have achieved high accuracy but are not widely applied in clinical practice. In our understanding, the existing methods have failed to establish the trust of sleep experts (e.g., physicians and clinical technologists) due to a lack of ability to explain the evidences/clues for scoring. In this study, we developed a deep-learning-based scoring model with a reasoning mechanism called class activation mapping (CAM) to solve this problem. This mechanism explicitly shows which portions of the signals support our model's sleep stage decision, and we verified that these portions overlap with the "characteristic waves," which are evidences/clues used in the manual scoring process. In exchange for the acquisition of explainability, employing CAM makes it difficult to follow some scoring rules. Although we concerned the negative effect of CAM on the scoring accuracy, we have found that the impact is limited. The evaluation experiment shows that the proposed model achieved a scoring accuracy of [Formula: see text]. It is superior to those of some existing methods and the inter-rater reliability among the sleep experts. These results suggest that Sleep-CAM achieved both explainability and required scoring accuracy for practical usage.
Topics: Data Collection; Electroencephalography; Polysomnography; Problem Solving; Reproducibility of Results; Sleep; Sleep Stages
PubMed: 35896616
DOI: 10.1038/s41598-022-16334-9