EEG Sleep Spindles Research Articles

Association of EEG characteristics with outcomes following pediatric ICU cardiac arrest: A secondary analysis of the ICU-RESUScitation trial

Background and ObjectivesThere are limited tools available following cardiac arrest to prognosticate neurologic outcomes. Prior retrospective and single center studies have demonstrated early EEG features are associated with neurologic outcome. This study aimed to evaluate the prognostic value of EEG for pediatric in-hospital cardiac arrest (IHCA) in a prospective, multicenter study. MethodsThis cohort study is a secondary analysis of the ICU-Resuscitation trial, a multicenter randomized interventional trial conducted at 18 pediatric and pediatric cardiac ICUs in the United States. Patients who achieved return of circulation (ROC) and had post-ROC EEG monitoring were eligible for inclusion. Patients < 90 days old and those with pre-arrest Pediatric Cerebral Performance Category (PCPC) scores > 3 were excluded. EEG features of interest included EEG Background Category, and presence of focal abnormalities, sleep spindles, variability, reactivity, periodic and rhythmic patterns, and seizures. The primary outcome was survival to hospital discharge with favorable neurologic outcome. Associations between EEG features and outcomes were assessed with multivariable logistic regression. Prediction models with and without EEG Background Category were developed and receiver operator characteristic curves compared. ResultsOf the 1129 patients with an index cardiac arrest who achieved ROC in the parent study, 261 had EEG within 24 h of ROC, of which 151 were evaluable. The cohort included 57% males with a median age of 1.1 years (IQR 0.4, 6.8). EEG features including EEG Background Category, sleep spindles, variability, and reactivity were associated with survival with favorable outcome and survival, (all p < 0.001). The addition of EEG Background Category to clinical models including age category, illness category, PRISM score, duration of CPR, first documented rhythm, highest early post-arrest arterial lactate improved the prediction accuracy achieving an AUROC of 0.84 (CI 0.77–0.92), compared to AUROC of 0.76 (CI 0.67–0.85) (p = 0.005) without EEG Background Category. ConclusionThis multicenter study demonstrates the value of EEG, in the first 24 h following ROC, for predicting survival with favorable outcome after a pediatric IHCA.

A personalized semi-automatic sleep spindle detection (PSASD) framework

BackgroundSleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone. New methodsA Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components. ResultsA single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures. Comparison with existing methodsPSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches. ConclusionsOverall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.

Effects of pleasant sound on overnight sleep condition: A crossover randomized study

It is desirable to improve sleep quality since poor sleep results in decreases in work productivity and increases in risks of lifestyle-related diseases. Sleep spindles in sleep EEG are waveforms that characterize non-REM sleep Stage 2 (Stage N2). Music therapy has been adopted as a non-pharmacological therapy for sleep quality improvement; however, few studies mention the relationship between music during sleep and spindles. We conducted a crossover randomized study to investigate music's effects on spindles and sleep parameters. Polysomnography (PSG) was performed on 12 adult males with sleep difficulties over three nights, during which they were exposed to three different acoustic environments–silent, white noise, and pleasant sounds–throughout the night, in a crossover randomized setting. Half of the participants with large WASO were defined as the sleep maintenance difficulty group. We investigated whether pleasant sounds shortened sleep onset latency (SOL) and increased the number of spindles (SN) and spindle density (SD) compared to white noise, using silent as the reference. The spindles were detected using the previously reported automatic spindle detection algorithm. After one patient was excluded due to data corruption, a total of 11 participants, including the sleep maintenance difficulty group (n = 5), were analyzed. For all participants, SOL was not significantly shorter with pleasant sound than with white noise (p = 0.683); for the sleep maintenance difficulty group, SOL tended to be shorter with pleasant sound than with white noise (p = 0.060). Compared to white noise, the SN increased in pleasant sound for 7 of 11 (4 of 5 in the sleep maintenance difficulty group), and SD increased for 5 of 11 (3 of 5 in the sleep maintenance difficulty group). The results suggest that all-night background sound exposure may affect SN and SD. Future research should investigate whether background sound exposure reduces sleep-related distress, achieves sound sleep, or improves daytime psychomotor function.

Entrainment to sleep spindles reflects dissociable patterns of connectivity between cortex and basal ganglia.

Sleep spindles are crucial for learning in the cortex and basal ganglia (BG) because they facilitate the reactivation of previously active neuronal ensembles. Studying field potentials (FPs) and spiking in the cortex and BG during sleep in non-human primates following pre-sleep learning, we show that FP sleep spindles are widespread in the BG and are similar to cortical spindles in morphology, spectral content, and response to the pre-sleep task. Further, BG spindles are concordant with electroencephalogram (EEG) spindles and associated with increased cortico-BG correlation. However, spindles across the BG differ markedly in their entrainment of local spiking. The spiking activity of striatal projection neurons exhibits consistent phase locking to striatal and EEG spindles, producing phase windows of peaked cross-region spindling. In contrast, firing in other BG nuclei is not entrained to either local or EEG sleep spindles. These results suggest corticostriatal synapses as the main hub for offline cortico-BG communication.

Mesial-Temporal Epileptic Ripples Correlate With Verbal Memory Impairment

RationaleHigh frequency oscillations (HFO; ripples = 80–200, fast ripples 200–500 Hz) are promising epileptic biomarkers in patients with epilepsy. However, especially in temporal epilepsies differentiation of epileptic and physiological HFO activity still remains a challenge. Physiological sleep-spindle-ripple formations are known to play a role in slow-wave-sleep memory consolidation. This study aimed to find out if higher rates of mesial-temporal spindle-ripples correlate with good memory performance in epilepsy patients and if surgical removal of spindle-ripple-generating brain tissue correlates with a decline in memory performance. In contrast, we hypothesized that higher rates of overall ripples or ripples associated with interictal epileptic spikes correlate with poor memory performance.MethodsPatients with epilepsy implanted with electrodes in mesial-temporal structures, neuropsychological memory testing and subsequent epilepsy surgery were included. Ripples and epileptic spikes were automatically detected in intracranial EEG and sleep-spindles in scalp EEG. The coupling of ripples to spindles was automatically analyzed. Mesial-temporal spindle-ripple rates in the speech-dominant-hemisphere (left in all patients) were correlated with verbal memory test results, whereas ripple rates in the non-speech-dominant hemisphere were correlated with non-verbal memory test performance, using Spearman correlation).ResultsIntracranial EEG and memory test results from 25 patients could be included. All ripple rates were significantly higher in seizure onset zone channels (p < 0.001). Patients with pre-surgical verbal memory impairment had significantly higher overall ripple rates in left mesial-temporal channels than patients with intact verbal memory (Mann–Whitney-U-Test: p = 0.039). Spearman correlations showed highly significant negative correlations of the pre-surgical verbal memory performance with left mesial-temporal spike associated ripples (rs = −0.458; p = 0.007) and overall ripples (rs = −0.475; p = 0.006). All three ripple types in right-sided mesial-temporal channels did not correlate with pre-surgical nonverbal memory. No correlation for the difference between post- and pre-surgical memory and pre-surgical spindle-ripple rates was seen in patients with left-sided temporal or mesial-temporal surgery.DiscussionThis study fails to establish a clear link between memory performance and spindle ripples. This highly suggests that spindle-ripples are only a small portion of physiological ripples contributing to memory performance. More importantly, this study indicates that spindle-ripples do not necessarily compromise the predictive value of ripples in patients with temporal epilepsy. The majority of ripples were clearly linked to areas with poor memory function.

Electroclinical spectrum of generalized paroxysmal fast activity in adults without epileptic encephalopathy.

Generalized paroxysmal fast activity (GPFA) is a rare and underreported EEG pattern known to be related to epileptic encephalopathy. We aimed to investigate the electroclinical spectrum of GPFA along with other atypical EEG features in patients without epileptic encephalopathy in routine EEG practice. Outpatient EEG records of Hacettepe University Hospital were retrospectively reviewed between 2010 and 2020. Patients ≥ 18years old with GPFA without epileptic encephalopathy were included. Electroclinical features of GPFA were analyzed. Atypical EEG features, epileptiform K-complexes and sleep spindles, and generalized polyspike train (GPT) were also investigated in this cohort. Most of the 19 included patients had refractory epilepsy (68%), while 16% were seizure-free. Generalized epilepsy (GE) was present in 58% of patients, and the rest had structural-focal epilepsy (26%), combined generalized and focal epilepsy (11%), or childhood occipital epilepsy (COE) (5%). Atypical EEG features with full atypical morphology were found in 91% of patients with GE. All patients with GPFA provoked by sleep had epileptiform K-complexes. The presence of GPT was not different between the GE and non-GE groups and was higher in patients with GPFA occurring only during sleep (p = 0.017). In two patients, GPFA frequency increased postictally. A transition from fixation-off sensitivity to GPFA occurred in a patient with COE. In this study, GPFA had a wide diagnostic range from focal to generalized epilepsy. The association of GPFA with other electroclinical features was of importance mostly for sleep outcomes; this finding might lead to a better understanding of epileptogenesis.

Spindle-AI: Sleep Spindle Number and Duration Estimation in Infant EEG.

Sleep spindle features show developmental changes during infancy and have the potential to provide an early biomarker for abnormal brain maturation. Manual identification of sleep spindles in the electroencephalogram (EEG) is time-consuming and typically requires highly-trained experts. Automated detection of sleep spindles would greatly facilitate this analysis. Research on the automatic detection of sleep spindles in infant EEG has been limited to-date. We present a random forest-based sleep spindle detection method (Spindle-AI) to estimate the number and duration of sleep spindles in EEG collected from 141 ex-term born infants, recorded at 4 months of age. The signal on channel F4-C4 was split into a training set (81 ex-term) and a validation set (30 ex-term). An additional 30 ex-term infant EEGs (channel F4-C4 and channel F3-C3) were used as an independent test set. Fourteen features were selected for input into a random forest algorithm to estimate the number and duration of spindles and the results were compared against sleep spindles annotated by an experienced clinical physiologist. The prediction of the number of sleep spindles in the independent test set demonstrated 93.3% to 93.9% sensitivity, 90.7% to 91.5% specificity, and 89.2% to 90.1% precision. The duration estimation of sleep spindle events in the independent test set showed a percent error of 5.7% to 7.4%. Spindle-AI has been implemented as a web server that has the potential to assist clinicians in the fast and accurate monitoring of sleep spindles in infant EEGs.

Sleep Disturbances in Rats With Genetic Pre-disposition to Spike-Wave Epilepsy (WAG/Rij)

Wistar Albino Glaxo Rijswijk (WAG/Rij) rats are widely used in basic and pre-clinical studies as a valid genetic model of absence epilepsy. Adult WAG/Rij rats exhibit generalized 8–10-Hz spike-wave discharges (SWDs) in the electroencephalogram. SWDs are known to result from thalamocortical circuit dysfunction, and this implies an intimate relationship between slow-wave EEG activity, sleep spindles, and SWDs. The present mini review summarizes relevant research on sleep-related disturbances associated with spike-wave epilepsy in WAG/Rij rats in the domain of slow-wave sleep EEG and microarousals. It also discusses enhancement of the intermediate stage of sleep. In general, sleep EEG studies provide important information about epileptogenic processes related to spike-wave epilepsy.

Scalp EEG markers of normal infant development using visual and computational approaches.

The infant brain is rapidly developing, and these changes are reflected in scalp electroencephalography (EEG) features, including power spectrum and sleep spindle characteristics. These biomarkers not only mirror infant development, but they are also altered by conditions such as epilepsy, autism, developmental delay, and trisomy 21. Prior studies of early development were generally limited by small cohort sizes, lack of a specific focus on infancy (0-2 years), and exclusive use of visual marking for sleep spindles. Therefore, we measured the EEG power spectrum and sleep spindles in 240 infants ranging from 0-24 months. To rigorously assess these metrics, we used both clinical visual assessment and computational techniques, including automated sleep spindle detection. We found that the peak frequency and power of the posterior dominant rhythm (PDR) increased with age, and a corresponding peak occurred in the EEG power spectra. Based on both clinical and computational measures, spindle duration decreased with age, and spindle synchrony increased with age. Our novel metric of spindle asymmetry suggested that peak spindle asymmetry occurs at 6-9 months of age.Clinical Relevance- Here we provide a robust characterization of the development of EEG brain rhythms during infancy. This can be used as a basis of comparison for studies of infant neurological disease, including epilepsy, autism, developmental delay, and trisomy 21.

Physiological Ripples Associated With Sleep Spindles Can Be Identified in Patients With Refractory Epilepsy Beyond Mesio-Temporal Structures.

Introduction: High frequency oscillations (HFO) are promising biomarkers of epileptic tissue. While group analysis suggested a correlation between surgical removal of HFO generating tissue and seizure free outcome, HFO could not predict seizure outcome on an individual patient level. One possible explanation is the lack of differentiation between physiological and epileptic HFO. In the mesio-temporal lobe, a proportion of physiological ripples can be identified by their association with scalp sleep spindles. Spike associated ripples in contrast can be considered epileptic. This study investigated whether categorizing ripples by the co-occurrence with sleep spindles or spikes improves outcome prediction after surgery. Additionally, it aimed to investigate whether spindle-ripple association is limited to the mesio-temporal lobe structures or visible across the whole brain.Methods: We retrospectively analyzed EEG of 31 patients with chronic intracranial EEG. Sleep spindles in scalp EEG and ripples and epileptic spikes in iEEG were automatically detected. Three ripple subtypes were obtained: SpindleR, Non-SpindleR, and SpikeR. Rate ratios between removed and non-removed brain areas were calculated. We compared the distinct ripple subtypes and their rates in different brain regions, inside and outside seizure onset areas and between patients with good and poor seizure outcome.Results: SpindleR were found across all brain regions. SpikeR had significantly higher rates in the SOZ than in Non-SOZ channels. A significant positive correlation between removal of ripple-events and good outcome was found for the mixed ripple group (rs = 0.43, p = 0.017) and for ripples not associated with spindles (rs=0.40, p = 0.044). Also, a significantly high proportion of spikes associated with ripples were removed in seizure free patients (p = 0.036).Discussion: SpindleR are found in mesio-temporal and neocortical structures, indicating that ripple-spindle-coupling might have functional importance beyond mesio-temporal structures. Overall, the proportion of SpindleR was low and separating spindle and spike associated ripples did not improve outcome prediction in our patient group. SpindleR analysis therefore can be a tool to identify physiological events but needs to be used in combination with other methods to have clinical relevance.

Consolidation and generalisation across sleep depend on individual EEG factors and sleep spindle density

Sleep is involved in both the consolidation of discrete episodes, as well as the generalisation of acquired memories into schemata. Here, we have isolated early versus late periods of sleep in order to replicate previous behavioural findings and to demonstrate: i) that distinct sleep and sleep electroencephalography (EEG) factors influence the generalisation of learned information, and; ii) that the consolidation and generalisation of memory across sleep depends on individual alpha frequency (IAF) and strength of initial encoding. Subjects underwent a night-half protocol with polysomnography (PSG), and completed a Chinese character-English paired associates learning task. Recognition accuracy of learned word-pairs, the extent to which the subject was able to generalise this knowledge, and the extent of explicit transfer of knowledge were measured. Results demonstrate that quality of initial learning determined the relationship between sleep neurophysiology and outcome, with IAF modulating this effect. We also note an effect of IAF in modulating the effect of sleep spindles in determining generalisation of learned materials. Finally, we note a complex relationship between initial learning, IAF and sleep spindle density in determining when information will reach explicit awareness across sleep. Together, these data implicate encoding factors in subsequent offline processing, demonstrate a potential role for individual differences in the EEG and subsequently add to our understanding of the the conditions in which sleep may benefit both memory and learning.

Treatment Timing, EEG, Neuroimaging, and Outcomes After Acute Necrotizing Encephalopathy in Children.

Acute necrotizing encephalopathy (ANE) is a rare condition associated with rapid progression to coma and high incidence of morbidity and mortality. Clinical, electroencephalographic (EEG), and brain magnetic resonance imaging (MRI) characteristics and immunomodulatory therapy timing were retrospectively analyzed in children with ANE. ANE severity scores (ANE-SS) and MRI scores were also assessed. The associations of patient characteristics with 6-month modified Rankin scale (mRS) and length of hospitalization were determined using either univariate linear regression or one-way analysis of variance. 7 children were retrospectively evaluated. Normal EEG sleep spindles (P = .024) and early treatment (R2 = .57, P = .030) were associated with improved outcomes (ie, decreased mRS). Higher ANE-SS (R2 = .79, P = .011), higher age (R2 = .62, P = .038), and presence of brainstem lesions (P = .015) were associated with longer length of hospitalization. Other patient characteristics were not significantly associated with mRS or length of hospitalization. Early immunomodulatory therapy and normal sleep spindles are associated with better functional outcome in children with ANE.

SpindleU-Net: An Adaptive U-Net Framework for Sleep Spindle Detection in Single-Channel EEG.

The sleep spindles in EEG have become one type of biomarker used to assess cognitive abilities and related disorders, and thus their detection is crucial for clinical research. This task, traditionally performed by sleep experts, is time-consuming. Many methods have been proposed to automate this process, yet an increase in performance is still expected. Inspired by the application in image segmentation, we propose a point-wise spindle detection method based on the U-Net framework with an attention module (SpindleU-Net). It maps the sequences of arbitrary-length EEG inputs to those of dense labels of spindle or non-spindle on freely chosen intervals. The attention module that focuses on the salient spindle region allows better performance, and a task-specific loss function is defined to alleviate the problem of imbalanced classification. As a deep learning method, SpindleU-Net outperforms state-of-the-art methods on the widely used benchmark dataset of MASS as well as the DREAMS dataset with a small number of samples. On MASS dataset it achieves average F1 scores of 0.854 and 0.803 according to its consistency with the annotations by two sleep experts respectively. On DREAMS dataset, it shows the average F1 score of 0.739. Its cross-dataset performance is also better compared to other methods, showing the good generalization ability for cross-dataset applications.

Random Forest-based Algorithm for Sleep Spindle Detection in Infant EEG.

Sleep spindles are associated with normal brain development, memory consolidation and infant sleep-dependent brain plasticity and can be used by clinicians in the assessment of brain development in infants. Sleep spindles can be detected in EEG, however, identifying sleep spindles in EEG recordings manually is very time-consuming and typically requires highly trained experts. Research on the automatic detection of sleep spindles in infant EEGs has been limited to-date. In this study, we present a novel supervised machine learning-based algorithm to detect sleep spindles in infant EEG recordings. EEGs collected from 141 ex-term born infants and 6 ex-preterm born infants, recorded at 4 months of age (adjusted), were used to train and test the algorithm. Sleep spindles were annotated by experienced clinical physiologists as the gold standard. The dataset was split into training (81 ex-term), validation (30 ex-term), and testing (30 ex-term + 6 ex-preterm) set. 15 features were selected for input into a random forest algorithm. Sleep spindles were detected in the ex-term infant EEG test set with 92.1% sensitivity and 95.2% specificity. For ex-preterm born infants, the sensitivity and specificity were 80.3% and 91.8% respectively. The proposed algorithm has the potential to assist researchers and clinicians in the automated analysis of sleep spindles in infant EEG.

A deep learning approach for real-time detection of sleep spindles

Objective. Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications. Approach. Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel. While the majority of spindle detection methods are used for off-line applications, our method is well suited for online applications. Main results. Compared with other spindle detection methods, SpindleNet achieves superior detection accuracy and speed, as demonstrated in two publicly available expert-validated EEG sleep spindle datasets. Our real-time detection of spindle onset achieves detection latencies of 150–350 ms (~two–three spindle cycles) and retains excellent performance under low EEG sampling frequencies and low signal-to-noise ratios. SpindleNet has good generalization across different sleep datasets from various subject groups of different ages and species. Significance. SpindleNet is ultra-fast and scalable to multichannel EEG recordings, with an accuracy level comparable to human experts, making it appealing for long-term sleep monitoring and closed-loop neuroscience experiments.

A Big-Data-Analytics Framework for Supporting Classification of ADHD and Healthy Children via Principal Component Analysis of EEG Sleep Spindles Power Spectra

Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is essentially clinical and research of biomarkers represents a current great challenge. The interest in sleep spindle has been increased after the description of their role in cognitive functions and of their involvement in neurodevelopmental disorders. We aimed to investigate this peculiar aspect of sleep through EEG spectral analysis of three different spindle epochs (ante, spindle, post), in order to provide more and detailed information on sleep brain functioning in ADHD. These features can be analyzed via well-known big data analytics methods. In our case, they were evaluated by using classification methods to support ADHD diagnosis. We combined ADHD’s related PSD features (i.e. theta, beta and sigma bands) with principal component analysis (PCA) for data dimensional reduction, and Linear Supported Vector Machine (Linear-SVM) as classification algorithm. In all bands and epochs, power values in Control group were higher than in ADHD children, although not statistically significant in all cases. Significant differences between ADHD and Control group were not detected for spindle epoch, while for ante and post epochs spectral power differed significantly in theta, beta and sigma bands. Results highlighted the possibility of using our new approach as a possible hallmark for ADHD. Indeed the analysis of PSD parameters combined with PCA and Linear-SVM classification resulted in a highly (94.1%) accurate discrimination between the two groups. The novelty of the approach is PSD analysis of different sleep spindles epochs combined with principal component analysis and Linear Supported Vector Machine classification. This study demonstrated the importance of analyzing sleep microstructures in ADHD. Encouraging results supports the potentiality of using EEG measures with specific methodologies we applied and should be confirmed in a large clinical study.

Spindler: a framework for parametric analysis and detection of spindles in EEG with application to sleep spindles

Objective. EEG spindles, narrow-band oscillatory signal bursts, are widely-studied biomarkers of subject state and neurological function. Most existing methods for spindle detection select algorithm parameters by optimizing agreement with expert labels. We propose a new framework for selecting algorithm parameters based on stability of spindle properties and elucidate the dependence of these properties on parameter selection for several algorithms. Approach. To demonstrate this approach we developed a new algorithm (Spindler) that decomposes the signal using matching pursuit with Gabor atoms and computes the spindles for each point in a fine grid of parameter values. After computing characteristic surfaces as a function of parameters, Spindler selects algorithm parameters based on the stability of characteristic surface geometry. Main results. Spindler performs well relative to several common supervised and unsupervised EEG sleep spindle detection methods. Spindler is available as an open-source MATLAB toolbox (https://github.com/VisLab/EEG-Spindles). In addition to Spindler, the toolbox provides implementations of several other spindle detection algorithms as well as standardized methods for matching ground truth to predictions and a framework for understanding algorithm parameter surfaces. Significance. This work demonstrates that parameter selection based on physical constraints rather than labelled data can provide effective, fully-automated, unsupervised spindle detection. This work also exposes the dangers of applying cross-validation without considering the dependence of spindle properties on parameters. Parameters selected to optimize one performance metric or matching method are not optimized for others. Furthermore, elucidation of the stability of predicted indicators with respect to algorithm parameter selection is critical to practical application of these algorithms.

Absent sleep EEG spindle activity in GluA1 (Gria1) knockout mice: relevance to neuropsychiatric disorders

Sleep EEG spindles have been implicated in attention, sensory processing, synaptic plasticity and memory consolidation. In humans, deficits in sleep spindles have been reported in a wide range of neurological and psychiatric disorders, including schizophrenia. Genome-wide association studies have suggested a link between schizophrenia and genes associated with synaptic plasticity, including the Gria1 gene which codes for the GluA1 subunit of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor. Gria1−/− mice exhibit a phenotype relevant for neuropsychiatric disorders, including reduced synaptic plasticity and, at the behavioural level, attentional deficits leading to aberrant salience. In this study we report a striking reduction of EEG power density including the spindle-frequency range (10–15 Hz) during sleep in Gria1−/− mice. The reduction of spindle-activity in Gria1−/− mice was accompanied by longer REM sleep episodes, increased EEG slow-wave activity in the occipital derivation during baseline sleep, and a reduced rate of decline of EEG slow wave activity (0.5–4 Hz) during NREM sleep after sleep deprivation. These data provide a novel link between glutamatergic dysfunction and sleep abnormalities in a schizophrenia-relevant mouse model.

An efficient approach for EEG sleep spindles detection based on fractal dimension coupled with time frequency image

Detection of the characteristics of the sleep stages, such as sleep spindles and K-complexes in EEG signals, is a challenging task in sleep research as visually detecting them requires high skills and efforts from sleep experts. In this paper, we propose a robust method based on time frequency image (TFI) and fractal dimension (FD) to detect sleep spindles in EEG signals. The EEG signals are divided into segments using a sliding window technique. The window size is set to 0.5 s with an overlapping of 0.4 s. A short time Fourier transform (STFT) is applied to obtain a TFI from each EEG segment. Each TFI is converted into an 8-bit binary image. Then, a box counting method is applied to estimate and discover the FDs of EEG signals. Different sets of features are extracted from each TFI after applying a statistical model to the FD of each TFI. The extracted statistical features are fed to a least square support vector machine (LS_SVM) to figure out the best combination of the features. As a result, the proposed method is found to have a high classification rate with the eight features sets. To verify the effectiveness of the proposed method, different classifiers, including a K-means, Naive Bayes and a neural network, are also employed. In this paper, the proposed method is evaluated using two publically available datasets: Dream sleep spindles and Montreal archive of sleep studies. The proposed method is compared with the current existing methods, and the results revealed that the proposed method outperformed the others. An average accuracy of 98.6% and 97.1% is obtained by the proposed method for the two datasets, respectively.

Glua1 knockout mice show reduced EEG sleep spindle activity without presenting long-term memory deficits

Glua1 knockout mice show reduced EEG sleep spindle activity without presenting long-term memory deficits