EEG Sleep Stages Research Articles

Transcranial Electrical Stimulation targeting limbic cortex increases the duration of human deep sleep

BackgroundResearchers have proposed that impaired sleep may be a causal link in the progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). Several recent findings suggest that enhancing deep sleep (N3) may improve neurological health in persons with MCI, and buffer the risk for AD. Specifically, Transcranial Electrical Stimulation (TES) of frontal brain areas, the inferred source of the Slow Oscillations (SOs) of N3 sleep, can extend N3 sleep duration and improve declarative memory for recently learned information. Recent work in our laboratory using dense array Electroencephalography (dEEG) localized the sources of SOs to anterior limbic sites – suggesting that targeting these sites with TES may be more effective for enhancing N3. MethodsFor the present study, we recruited 13 healthy adults (M = 42 years) to participate in three all-night sleep EEG recordings where they received low level (0.5 mA) TES designed to target anterior limbic areas and a sham stimulation (placebo). We used a convolutional neural network, trained and tested on professionally scored EEG sleep staging, to predict sleep stages for each recording. ResultsWhen compared to the sham session, limbic-targeted TES significantly increased the duration of N3 sleep. TES also significantly increased spectral power in the 0.5–1 Hz frequency band (relative to pre-TES epochs) in left temporoparietal and left occipital scalp regions compared to sham. ConclusionThese results suggest that even low-level TES, when specifically targeting anterior limbic sites, can increase deep (N3) sleep and thereby contribute to healthy sleep quality.

Automatic sleep staging with a single-channel EEG based on ensemble empirical mode decomposition

Objective evaluation of sleep is crucial to recognize sleep disorders and take effective interventions accordingly. Due to the shortcomings of the conventional polysomnographic acquisition, an automatic sleep scoring method based on a single-channel electroencephalogram (EEG) is vital in sleep medicine practice. In this study, we proposed a data-driven and robust automatic sleep staging scheme based on a single-channel EEG. With the decomposition of the EEG epochs using Ensemble Empirical Mode Decomposition (EEMD), we extracted various features using statistical, time-domain and nonlinear dynamics characteristics from not only the original EEG signal but also the decomposed intrinsic mode functions (IMFs). A classification model with the eXtreme Gradient Boosting (XGBoost) algorithm was trained and tested using 5-fold cross-validation on three different databases, i.e., the Sleep-EDF database available at Physionet, the DREAMS Subjects database and the database of Sleep Heart Health Study (SHHS). The results demonstrated that the analysis of IMFs derived from EEMD could provide substantial supplement to the classification of EEG sleep stages. In the tasks of 4-class and 5-class sleep staging, the proposed method achieved an accuracy of 93.1% and 91.9% for the Sleep-EDF database, 86.4% and 83.4% for the DREAMS Subjects database, and 87.5% and 85.8% for the SHHS database, respectively. Furthermore, our observation that prefrontal EEG derivations should be optimal choices might evoke promising application of wearable EEG devices on sleep monitoring because EEG signals can be easily obtained using dry electrodes on the forehead. Additionally, the proposed method is computationally efficient and should be valuable in real-time sleep-scoring.

Neonatal EEG sleep stage classification based on deep learning and HMM

Objective. Automatic sleep stage scoring is of great importance for investigating sleep architecture during infancy. In this work, we introduce a novel multichannel approach based on deep learning networks and hidden Markov models (HMM) to improve the accuracy of sleep stage classification in term neonates. Approach. The classification performance was evaluated on quiet sleep (QS) and active sleep (AS) stages, each with two sub-states, using multichannel EEG data recorded from sixteen neonates with postmenstrual age of 38–40 weeks. A comprehensive set of linear and nonlinear features were extracted from thirty-second EEG segments. The feature space dimensionality was then reduced by using an evolutionary feature selection method called MGCACO (Modified Graph Clustering Ant Colony Optimization) based on the relevance and redundancy analysis. A bi-directional long-short time memory (BiLSTM) network was trained for sleep stage classification. The number of channels was optimized using the sequential forward selection method to reduce the spatial space. Finally, an HMM-based postprocessing stage was used to reduce false positives by incorporating the knowledge of transition probabilities between stages into the classification process. The method performance was evaluated using the K-fold (KFCV) and leave-one-out cross-validation (LOOCV) strategies. Main results. Using six-bipolar channels, our method achieved a mean kappa and an overall accuracy of 0.71–0.76 and 78.9%–82.4% using the KFCV and LOOCV strategies, respectively. Significance. The presented automatic sleep stage scoring method can be used to study the neurodevelopmental process and to diagnose brain abnormalities in term neonates.

On the development of sleep states in the first weeks of life.

Human newborns spend up to 18 hours sleeping. The organization of their sleep differs immensely from adult sleep, and its quick maturation and fundamental changes correspond to the rapid cortical development at this age. Manual sleep classification is specifically challenging in this population given major body movements and frequent shifts between vigilance states; in addition various staging criteria co-exist. In the present study we utilized a machine learning approach and investigated how EEG complexity and sleep stages evolve during the very first weeks of life. We analyzed 42 full-term infants which were recorded twice (at week two and five after birth) with full polysomnography. For sleep classification EEG signal complexity was estimated using multi-scale permutation entropy and fed into a machine learning classifier. Interestingly the baby's brain signal complexity (and spectral power) revealed developmental changes in sleep in the first 5 weeks of life, and were restricted to NREM ("quiet") and REM ("active sleep") states with little to no changes in state wake. Data demonstrate that our classifier performs well over chance (i.e., >33% for 3-class classification) and reaches almost human scoring accuracy (60% at week-2, 73% at week-5). Altogether, these results demonstrate that characteristics of newborn sleep develop rapidly in the first weeks of life and can be efficiently identified by means of machine learning techniques.

EEG sleep stages identification based on weighted undirected complex networks

Background and ObjectiveSleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks. MethodsEach EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks. ResultsIn this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals. ConclusionsAn average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard.

Head cooling during sleep improves sleep quality in the luteal phase in female university students: A randomized crossover-controlled pilot study.

Although selective head-cooling has been reported to decrease scalp and tympanic temperature and improve sleep quality, whether head-cooling during sleep can improve sleep quality in women during the luteal phase has not been elucidated. This randomized, controlled crossover open trial aimed to investigate the effect of head cooling during sleep on sleep quality in women during the luteal phase. Female university students aged 19–25 years with increased daytime sleepiness during the luteal phase were recruited by poster advertisement at their university from May to June 2016 and from May to June 2017. Fourteen women aged 19–22 years participated in this study. The temperature-controllable cooling sheet containing tubes filled with circulating water was used for head-cooling, and the head-cooling and the controlled temperature were set at 25°C and 35°C, respectively. Electroencephalogram data were obtained using a single-channel portable electroencephalogram device. The difference in sleep-related variables and tympanic temperature between head-cooling and control were analyzed using a linear mixed model. The proportion of arousal was lower with head cooling than with the control. In contrast, the proportion of non-REM3 and the delta power were higher with head cooling than with the control. The proportion of non-REM2 and non-REM3 among sleep EEG stages were positively and negatively correlated with the mean tympanic temperature during sleep, respectively. However, arousal and REM were not correlated with tympanic temperature. We considered the reduction of arousal time by head-cooling might be related to scalp temperature rather than tympanic temperature. Further, our results suggested that head-cooling also improved subjective sleep comfort. In conclusion, head-cooling during sleep could improve sleep quality in young women during the luteal phase.

Sleep staging from single-channel EEG with multi-scale feature and contextual information.

Portable sleep monitoring devices with less-attached sensors and high-accuracy sleep staging methods can expedite sleep disorder diagnosis. The aim of this study was to propose a single-channel EEG sleep staging model, SleepStageNet, which extracts sleep EEG features by multi-scale convolutional neural networks (CNN) and then infers the type of sleep stages by capturing the contextual information between adjacent epochs using recurrent neural networks (RNN) and conditional random field (CRF). To verify the feasibility of our model, two datasets, one composed by two different single-channel EEGs (Fpz-Cz and Pz-Oz) on 20 healthy people and one composed by a single-channel EEG (F4-M1) on 104 obstructive sleep apnea (OSA) patients with different severities, were examined. The corresponding sleep stages were scored as four states (wake, REM, light sleep, and deep sleep). The accuracy measures were obtained from epoch-by-epoch comparison between the model and PSG scorer, and the agreement between them was quantified with Cohen's kappa (ҡ). Our model achieved superior performance with average accuracy (Fpz-Cz, 0.88; Pz-Oz, 0.85) and ҡ (Fpz-Cz, 0.82; Pz-Oz, 0.77) on the healthy people. Furthermore, we validated this model on the OSA patients with average accuracy (F4-M1, 0.80) and ҡ (F4-M1, 0.67). Our model significantly improved the accuracy and ҡ compared to previous methods. The proposed SleepStageNet has proved feasible for assessment of sleep architecture among OSA patients using single-channel EEG. We suggest that this technological advancement could augment the current use of home sleep apnea testing.

Recurrent Deep Neural Networks for Real-Time Sleep Stage Classification From Single Channel EEG.

Objective: We investigate the design of deep recurrent neural networks for detecting sleep stages from single channel EEG signals recorded at home by non-expert users. We report the effect of data set size, architecture choices, regularization, and personalization on the classification performance.Methods: We evaluated 58 different architectures and training configurations using three-fold cross validation.Results: A network consisting of convolutional (CONV) layers and long short term memory (LSTM) layers can achieve an agreement with a human annotator of Cohen's Kappa of ~0.73 using a training data set of 19 subjects. Regularization and personalization do not lead to a performance gain.Conclusion: The optimal neural network architecture achieves a performance that is very close to the previously reported human inter-expert agreement of Kappa 0.75.Significance: We give the first detailed account of CONV/LSTM network design process for EEG sleep staging in single channel home based setting.

Automated EEG sleep staging in the term-age baby using a generative modelling approach

Objective. We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38–40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader quiet sleep (QS) and active sleep (AS) stages to four, more complex states, and the quality and timing of this differentiation is indicative of the level of brain development. However, existing methods for automated sleep classification remain focussed only on QS and AS sleep classification. Approach. EEG features were calculated from 16 EEG recordings, in 30 s epochs, and personalized feature scaling used to correct for some of the inter-recording variability, by standardizing each recording’s feature data using its mean and standard deviation. Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) were trained, with the HMM incorporating knowledge of the sleep state transition probabilities. Performance of the GMM and HMM (with and without scaling) were compared, and Cohen’s kappa agreement calculated between the estimates and clinicians’ visual labels. Main results. For four-state classification, the HMM proved superior to the GMM. With the inclusion of personalized feature scaling, mean kappa (±standard deviation) was 0.62 (±0.16) compared to the GMM value of 0.55 (±0.15). Without feature scaling, kappas for the HMM and GMM dropped to 0.56 (±0.18) and 0.51 (±0.15), respectively. Significance. This is the first study to present a successful method for the automated staging of four states in term-age sleep using multichannel EEG. Results suggested a benefit in incorporating transition information using an HMM, and correcting for inter-recording variability through personalized feature scaling. Determining the timing and quality of these states are indicative of developmental delays in both preterm and term-born babies that may lead to learning problems by school age.

Validation of HRV-based drowsy-driving detection method with EEG sleep stage classification

Validation of HRV-based drowsy-driving detection method with EEG sleep stage classification

P.1.b.011 - Heart rate variability discriminates between sleep-EEG stages in healthy adults; proposal of a standard rule of assessment during sleep

P.1.b.011 - Heart rate variability discriminates between sleep-EEG stages in healthy adults; proposal of a standard rule of assessment during sleep

Automatic quiet sleep detection based on multifractality in preterm neonates: Effects of maturation.

This study investigates the multifractal formalism framework for quiet sleep detection in preterm babies. EEG recordings from 25 healthy preterm infants were used in order to evaluate the performance of multifractal measures for the detection of quiet sleep. Results indicate that multifractal analysis based on wavelet leaders is able to identify quiet sleep epochs, but the classifier performances seem to be highly affected by the infant's age. In particular, from the developed classifiers, the lowest area under the curve (AUC) has been obtained for EEG recordings at very young age (≤ 31 weeks post-menstrual age), and the maximum at full-term age (≥ 37 weeks post-menstrual age). The improvement in classification performances can be due to a change in the multifractality properties of neonatal EEG during the maturation of the infant, which makes the EEG sleep stages more distinguishable.

Complex networks approach for EEG signal sleep stages classification

Sleep stage scoring is a challenging task. Most of existing sleep stage classification approaches rely on analysing electroencephalography (EEG) signals in time or frequency domain. A novel technique for EEG sleep stages classification is proposed in this paper. The statistical features and the similarities of complex networks are used to classify single channel EEG signals into six sleep stages. Firstly, each EEG segment of 30s is divided into 75 sub-segments, and then different statistical features are extracted from each sub-segment. In this paper, feature extraction is important to reduce dimensionality of EEG data and the processing time in classification stage. Secondly, each vector of the extracted features, which represents one EEG segment, is transferred into a complex network. Thirdly, the similarity properties of the complex networks are extracted and classified into one of the six sleep stages using a k-means classifier. For further investigation, in the statistical features extraction phase two statistical features sets are tested and ranked based on the performance of the complex networks. To investigate the classification ability of complex networks combined with k-means, the extracted statistical features were also forwarded to a k-means and a support vector machine (SVM) for comparison. We also compare the proposed method with other existing methods in the literature. The experimental results show that the proposed method attains better classification results and a reasonable execution time compared with the SVM, k-means and the other existing methods. The research results in this paper indicate that the proposed method can assist neurologists and sleep specialists in diagnosing and monitoring sleep disorders.

Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain.

The main objective of this study was to enhance the performance of sleep stage classification using single-channel electroencephalograms (EEGs), which are highly desirable for many emerging technologies, such as telemedicine and home care. The proposed method consists of decomposing EEGs by a discrete wavelet transform and computing the kurtosis, skewness and variance of its coefficients at selected levels. A random forest predictor is trained to classify each epoch into one of the Rechtschaffen and Kales' stages. By performing a comprehensive set of tests on 106,376 epochs available from the Physionet public database, it is demonstrated that the use of these three statistical moments has enhanced performance when compared to their application in the time domain. Furthermore, the chosen set of features has the advantage of exhibiting a stable classification performance for all scoring systems, i.e., from 2- to 6-state sleep stages. The stability of the feature set is confirmed with ReliefF tests which show a performance reduction when any individual feature is removed, suggesting that this group of feature cannot be further reduced. The accuracies and kappa coefficients yield higher than 90% and 0.8, respectively, for all of the 2- to 6-state sleep stage classification cases.

“Microcyclic” Changes in Brain Bioelectrical Activity at Different Stages of Natural Sleep in Humans

We report here data on a number of systematically recorded phasic EEG phenomena during the sleep–waking cycle, along with data on the heterogeneity of the EEG stages of sleep forming sequential sleep macrocycles in humans. These transient EEG changes quite often showed a tendency to periodicity, especially at the initial stages of sleep and in transitional states; characteristic sleep microcycles were observed. An attempt to identify the microstructures of EEG sleep stages during the sleep–waking cycle was made by identifying seven clusters reflecting changes in the biopotential field of the brain represented in n-dimensional factorial space. Most sleep stages were found to consist of three or four clusters, though some sleep periods (especially Loomis stage B and 1REM) were more homogeneous in structure. It is suggested that this heterogeneity of the microstructure of the spatial organization of oscillations in brain biopotentials in particular sleep stages reflects the dynamics of neurophysiological processes, promoting more effective performance of the repair and homeostatic functions of sleep.

“Микроциклические” изменения биоэлектрической активности мозга в различных стадиях естественного сна у человека

This study presents data on some phasic EEG phenomena regulary recorded during sleep - wakefulness cycle and heterogeneity of EEG sleep stages forming consecutive human sleep macrocycles. These short-term EEG changes quite often tend to periodization, especially during initial sleep-stages and transitive states (sleep microcycles). An attempt was made to reveal EEG microstructures in sleep - wakefulness cycle on the basis of 7 clusters reflecting changes in the biopotential field of the brain represented in n-dimensional factorial space. It was found that most sleep stages were presented by 3-4 clusters, but some sleep periods (such as B (Loomis, 1937) and IREM) showed more homogeneous structure. It was suggested that the heterogeneity of the EEG spatial organization within sleep stages reflect the dynamics of neurophysiological processes underlying reparative and homeostatic sleep functions.

Optimal channel selection for analysis of EEG-sleep patterns of neonates

This paper extends our previous work on automated detection and classification of neonate EEG sleep stages. In [19] we adapted and integrated a range of computational, mathematical and statistical tools for the analysis of neonatal electroencephalogram (EEG) sleep recordings with the aim of facilitating the assessment of neonatal brain maturation and dismaturity by studying the structure and temporal patterns of their sleep. That work relied on algorithms using a single channel of EEG. The present paper builds on our previous work by incorporating a larger selection of EEG channels that capture both the spatial distribution and temporal patterns of EEG during sleep. Using a multivariate analysis approach, we obtain the “optimal” selection of the EEG channels and characteristics that are most suitable for EEG sleep state separation.

Developmental aspects of sleep in Prader Willi Syndrome

Background: Children with Prader Willi syndrome (PWS) are at risk of sleep disordered breathing involving central and obstructive components. PWS patients are known to have abnormal responses to hypoxia and hypercapnoea, an increased incidence of Obstructive Sleep Apnoea [OSA] and hypersomnolence when older. As infants, many present with delay in central control of breathing during sleep and resultant hypoxia. This may have long term consequences for the phenotype. Methods: Polysomnography (PSG) and multiple sleep latency testing [MSLT] was performed on PWS children attending a multidisciplinary clinic. Sleep disorders and age of presentation were identified. EEG spectral analysis on infants was undertaken and compared to a group of typically developing infants. Genotype was established. Results: Results suggest that in children less than 2 years central hypoventilation is present in 100%. 10% of children older than five had normal studies and the rest had OSA+/central hypoventilation. 10% had central hypoventilation and hypersomnolence. Twenty‐five children under 12 months of age were analysed separately. Improvements in central hypoventilation were seen in some infants as they matured. The relationship between this and development of mature EEG patterns was examined and is significantly different to EEG spectral analysis of normal infants studied at 3, 6 and 12 months. This was also related to the genotype of each infant. Conclusion: Infants with Prader Willi have a delay in neural maturity as reflected by EEG development of defined EEG sleep stages. All infants with Prader Willi should have a sleep study in infancy and appropriate therapy instigated if required. Hypersomnolence is generally associated with other sleep disorders.

The Visual Scoring of Sleep in Adults

The 1968 Rechtschaffen and Kales (R & K) sleep scoring manual was published 15 years after REM sleep was discovered. Advances in the ensuing 28 years warranted a re-look at visual scoring of sleep stages. This paper describes the work of the AASM Visual Scoring Task Force, including methodology, a literature review and the rationale behind the new rules. Reliability studies of R & K scoring were reviewed; reliability was low for stage one and moderate for slow wave sleep. Evidence indicated that K complexes and slow waves are expressed maximal frontally, spindles centrally and alpha rhythm over the occipital region. Three derivations of EEG, two of electro-oculography, and one of chin EMG were recommended. Scoring by 30-second epochs was retained. New terminology for sleep stages was proposed. Attenuation of alpha rhythm was determined to be the most valid electrophysiological marker of sleep onset. Alternative measures were proposed for non-alpha generating subjects. K complexes associated with arousals were determined to be insufficient alone to define the new stage N2. No evidence was found to justify dividing slow wave sleep into two stages. No reasons were found to alter the current slow wave amplitude criteria at any age. The phenomena of REM sleep were defined. The rules for defining onset and termination of REM sleep periods were simplified. Movement time was eliminated and major body movements defined. Studies are needed to test the reliability of the new rules. Future advances in technology may require modification of these rules with time.

P04.7 Automated analysis of neonatal EEG-Sleep stages in preterm and full term infants

P04.7 Automated analysis of neonatal EEG-Sleep stages in preterm and full term infants