Temporal and Potential Predictive Relationships between Sleep Spindle Density and Spike-and-Wave Discharges.

Spike-and-wave discharges (SWDs) and sleep spindles are characteristic electroencephalographic (EEG) hallmarks of absence seizures and nonrapid eye movement sleep, respectively. They are commonly generated by the cortico-thalamo-cortical network including the thalamic reticular nucleus (TRN). It has been reported that SWD development is accompanied by a decrease in sleep spindle density in absence seizure patients and animal models. However, whether the decrease in sleep spindle density precedes, coincides with, or follows, the SWD development remains unknown. To clarify this, we exploited Pvalb-tetracycline transactivator (tTA)::tetO-ArchT (PV-ArchT) double-transgenic mouse, which can induce an absence seizure phenotype in a time-controllable manner by expressing ArchT in PV neurons of the TRN. In these mice, EEG recordings demonstrated that a decrease in sleep spindle density occurred 1 week before the onset of typical SWDs, with the expression of ArchT. To confirm such temporal relationship observed in these genetic model mice, we used a gamma-butyrolactone (GBL) pharmacological model of SWDs. Prior to GBL administration, we administered caffeine to wild-type mice for 3 consecutive days to induce a decrease in sleep spindle density. We then administered low-dose GBL, which cannot induce SWDs in normally conditioned mice but led to the occurrence of SWDs in caffeine-conditioned mice. These findings indicate a temporal relationship in which the decrease in sleep spindle density consistently precedes SWD development. Furthermore, the decrease in sleep spindle activity may have a role in facilitating the development of SWDs. Our findings suggest that sleep spindle reductions could serve as early indicators of seizure susceptibility.

How to quantify sleepiness during an attempt to sleep?

Background. Sleepiness assessment tools were mostly developed for detection of an elevated sleepiness level in the condition of sleep deprivation and several medical conditions. However, sleepiness occurs in various other conditions including the transition from wakefulness to sleep during an everyday attempt to get sleep.Objective. We examined whether objective sleepiness indexes can be implicated in detection of fluctuations in sleepiness level during the polysomnographically-monitored attempt to sleep, i.e. in the absence of self-reports on perceived sleepiness level throughout such an attempt.Approach. The polysomnographic signals were recorded in the afternoon throughout 106 90 min napping attempts of 53 university students (28 females). To calculate two objective sleepiness indexes, the electroencephalographic (EEG) spectra were averaged on 30 s epochs of each record, assigned to one of 5 sleep-wake stages, and scored using either the frequency weighting curve for sleepiness substate of wake state or loadings of each frequency on the 2nd principal component of variation in the EEG spectrum (either sleepiness score or PC2 score, respectively).Main results. We showed that statistically significant fluctuations in these two objective sleepiness indexes during epochs assigned to wake stage can be described in terms of the changes in verbally anchored levels of subjective sleepiness assessed by scoring on the 9-step Karolinska Sleepiness Scale.Significance. The results afford new opportunities to elaborate importance of intermediate substates between wake and sleep states for sleep-wake dynamics in healthy individuals and patients with disturbed sleep.

An ensemble method for improving robustness against the electrode contact problems in automated sleep stage scoring.

In-home automated scoring systems are in high demand; however, the current systems are not widely adopted in clinical settings. Problems with electrode contact and restriction on measurable signals often result in unstable and inaccurate scoring for clinical use. To address these issues, we propose a method based on ensemble of small sleep stage scoring models with different input signal sets. By excluding models that employ problematic signals from the voting process, our method can mitigate the effects of electrode contact failure. Comparative experiments demonstrated that our method could reduce the impact of contact problems and improve scoring accuracy for epochs with problematic signals by 8.3 points, while also decreasing the deterioration in scoring accuracy from 7.9 to 0.3 points compared to typical methods. Additionally, we confirmed that assigning different input sets to small models did not diminish the advantages of the ensemble but instead increased its efficacy. The proposed model can improve overall scoring accuracy and minimize the effect of problematic signals simultaneously, making in-home sleep stage scoring systems more suitable for clinical practice.

Multi-Index Driver Drowsiness Detection Method Based on Driver's Facial Recognition Using Haar Features and Histograms of Oriented Gradients.

It is estimated that 10% to 20% of road accidents are related to fatigue, with accidents caused by drowsiness up to twice as deadly as those caused by other factors. In order to reduce these numbers, strategies such as advertising campaigns, the implementation of driving recorders in vehicles used for road transport of goods and passengers, or the use of drowsiness detection systems in cars have been implemented. Within the scope of the latter area, the technologies used are diverse. They can be based on the measurement of signals such as steering wheel movement, vehicle position on the road, or driver monitoring. Driver monitoring is a technology that has been exploited little so far and can be implemented in many different approaches. This work addresses the evaluation of a multidimensional drowsiness index based on the recording of facial expressions, gaze direction, and head position and studies the feasibility of its implementation in a low-cost electronic package. Specifically, the aim is to determine the driver's state by monitoring their facial expressions, such as the frequency of blinking, yawning, eye-opening, gaze direction, and head position. For this purpose, an algorithm capable of detecting drowsiness has been developed. Two approaches are compared: Facial recognition based on Haar features and facial recognition based on Histograms of Oriented Gradients (HOG). The implementation has been carried out on a Raspberry Pi, a low-cost device that allows the creation of a prototype that can detect drowsiness and interact with peripherals such as cameras or speakers. The results show that the proposed multi-index methodology performs better in detecting drowsiness than algorithms based on one-index detection.

Overnight Sleep Staging Using Chest-Worn Accelerometry.

Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform "proxy" sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13-83 years, with BMI 18-47 kg/m2. We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen's kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine.

Oscillatory-Quality of sleep spindles links brain state with sleep regulation and function.

Here, we characterized the dynamics of sleep spindles, focusing on their damping, which we estimated using a metric called oscillatory-Quality (o-Quality), derived by fitting an autoregressive model to electrophysiological signals, recorded from the cortex in mice. The o-Quality of sleep spindles correlates weakly with their amplitude, shows marked laminar differences and regional topography across cortical regions, reflects the level of synchrony within and between cortical networks, is strongly modulated by sleep-wake history, reflects the degree of sensory disconnection, and correlates with the strength of coupling between spindles and slow waves. As most spindle events are highly localized and not detectable with conventional low-density recording approaches, o-Quality thus emerges as a valuable metric that allows us to infer the spread and dynamics of spindle activity across the brain and directly links their spatiotemporal dynamics with local and global regulation of brain states, sleep regulation, and function.

Wireless ear EEG to monitor drowsiness.

Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.

GRU-powered sleep stage classification with permutation-based EEG channel selection.

We present a new approach to classifying the sleep stage that incorporates a computationally inexpensive method based on permutations for channel selection and takes advantage of deep learning power, specifically the gated recurrent unit (GRU) model, along with other deep learning methods. By systematically permuting the electroencephalographic (EEG) channels, different combinations of EEG channels are evaluated to identify the most informative subset for the classification of the 5-class sleep stage. For analysis, we used an EEG dataset that was collected at the International Institute for Integrative Sleep Medicine (WPI-IIIS) at the University of Tsukuba in Japan. The results of these explorations provide many new insights such as the (1) drastic decrease in performance when channels are fewer than 3, (2) 3-random channels selected by permutation provide the same or better prediction than the 3 channels recommended by the American Academy of Sleep Medicine (AASM), (3) N1 class suffers the most in prediction accuracy as the channels drop from 128 to 3 random or 3 AASM, and (4) no single channel provides acceptable levels of accuracy in the prediction of 5 classes. The results obtained show the GRU's ability to retain essential temporal information from EEG data, which allows capturing the underlying patterns associated with each sleep stage effectively. Using permutation-based channel selection, we enhance or at least maintain as high model efficiency as when using high-density EEG, incorporating only the most informative EEG channels.

Common sleep data pipeline for combined data sets.

Over the past few years, sleep research has shown impressive performance of deep neural networks in the area of automatic sleep-staging. Recent studies have demonstrated the necessity of combining multiple data sets to obtain sufficiently generalizing results. However, working with large amounts of sleep data can be challenging, both from a hardware perspective and because of the different preprocessing steps necessary for distinct data sources. Here we review the possible obstacles and present an open-source pipeline for automatic data loading. Our solution includes both a standardized data store as well as a 'data serving' portion which can be used to train neural networks on the standardized data, allowing for different configuration options for different studies and machine learning designs. The pipeline, including implementation, is made public to ensure better and more reproducible sleep research.

Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers.

Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed, and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite improved performance, a limitation of most deep-learning based algorithms is their black-box behavior, which have limited their use in clinical settings. Here, we propose a cross-modal transformer, which is a transformer-based method for sleep stage classification. The proposed cross-modal transformer consists of a cross-modal transformer encoder architecture along with a multi-scale one-dimensional convolutional neural network for automatic representation learning. The performance of our method is on-par with the state-of-the-art methods and eliminates the black-box behavior of deep-learning models by utilizing the interpretability aspect of the attention modules. Furthermore, our method provides considerable reductions in the number of parameters and training time compared to the state-of-the-art methods. Our code is available at https://github.com/Jathurshan0330/Cross-Modal-Transformer. A demo of our work can be found at https://bit.ly/Cross_modal_transformer_demo.

Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis.

Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems.

Differentiation Across Bipolar Disorder and Major Depressive Disorder by Whole-Night Polysomnographic Findings.

Abstract.Background: There is growing evidence that understanding the role of sleep disturbance in bipolar disorder (BD) and major depressive disorder (MDD) is helpful when studying the high heterogeneity of patients across psychiatric disorders.Objective: The present study was designed to investigate the transdiagnostic role of sleep disturbance measured by polysomnography (PSG) in differentiating from MDD with BD.Methods: A total of 256 patients with MDD and 107 first-episode and never medicated patients with BD using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria were recruited. All patients completed 1 night of PSG recording, and the changes in objective sleep structure parameters were determined by PSG analysis.Results: We showed that patients with MDD had statistically longer rapid eye movement (REM) latency, a higher percentage of stage N2 sleep, and lower percentages of stage N3 sleep and REM sleep than those with BD after controlling for confounding factors (all P < .05). Moreover, using the logistic regression analysis, we identified that REM latency was associated with BD diagnosis among the PSG sleep features. The cutoff value for PSG characteristics to differentiate BD from MDD was 261 in REM latency (sensitivity: 41.4% and specificity: 84.1%).Conclusions: Our findings suggest that PSG-measured sleep abnormalities, such as reduced REM latency, may be a diagnostic differentiating factor between MDD and BD, indicating their roles in identifying homogeneous transdiagnostic subtypes across psychiatric disorders.

The effect of central sleep apnea on sleep architecture in children with obstructive sleep apnea.

OBJECTIVE: This study aimed to investigate how central sleep apnea (CSA) impacts sleep patterns in children with obstructive sleep apnea (OSA). METHODS: Children undergoing polysomnography (PSG) were enrolled and sorted into two groups: those with OSA alone (Group A) and those with both OSA and CSA (CAI <1 nd: children with 10 % CSA or more and less than 50 %, Group B). Statistical analysis was conducted to compare sleep structure and clinical features between Group A and Group B. RESULTS: Group B exhibited significantly higher respiratory events, apnea hypoventilation index, apnea index and oxygen desaturation index (ODI) compared to Group A (p < 0.05). Group B also showed higher total sleep time and arousal index than Group A (P < 0.05). The proportion of time spent in stage N3 was lower in Group B than in Group A (P < 0.05). Moreover, mean heart rate and minimum heart rate were higher in Group B compared to Group A (P < 0.05).Minimum oxygenation levels (including non-rapid eye movement (NREM) stages) were lowe in Group B than in Group A (P < 0.05). Additionally, the prevalence of positional obstructive sleep apnea (P-OSA) was greater in Group B than in Group A (P < 0.05). CONCLUSION: In comparison to those with OSA alone, children with OSA and concurrent CSA exhibited distinct sleep patterns, including reduced N3uration, higher arousal index, longer respiratory events, higher ODI, and lower oxygen saturation, higher heart rate.

Cerebral blood flow in sleep: A systematic review and meta-analysis.

Sleep plays an essential role in physiology, allowing the brain and body to restore itself. Despite its critical role, our understanding of the underlying processes in the sleeping human brain is still limited. Sleep comprises several distinct stages with varying depths and temporal compositions. Cerebral blood flow (CBF), which delivers essential nutrients and oxygen to the brain, varies across brain regions throughout these sleep stages, reflecting changes in neuronal function and regulation. This systematic review and meta-analysis assesses global and regional CBF across sleep stages. We included, appraised, and summarized all 38 published sleep studies on CBF in healthy humans that were not or only slightly (<24 h) sleep deprived. Our main findings are that CBF varies with sleep stage and depth, being generally lowest in NREM sleep and highest in REM sleep. These changes appear to stem from sleep stage-specific regional brain activities that serve particular functions, such as alterations in consciousness and emotional processing.

MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice.

Over the past decades, feature-based statistical machine learning and deep neural networks have been extensively utilized for automatic sleep stage classification (ASSC). Feature-based approaches offer clear insights into sleep characteristics and require low computational power but often fail to capture the spatial-temporal context of the data. In contrast, deep neural networks can process raw sleep signals directly and deliver superior performance. However, their overfitting, inconsistent accuracy, and computational cost were the primary drawbacks that limited their end-user acceptance. To address these challenges, we developed a novel neural network model, MLS-Net, which integrates the strengths of neural networks and feature extraction for automated sleep staging in mice. MLS-Net leverages temporal and spectral features from multimodal signals, such as EEG, EMG, and eye movements (EMs), as inputs and incorporates a bidirectional Long Short-Term Memory (bi-LSTM) to effectively capture the spatial-temporal nonlinear characteristics inherent in sleep signals. Our studies demonstrate that MLS-Net achieves an overall classification accuracy of 90.4% and REM state precision of 91.1%, sensitivity of 84.7%, and an F1-Score of 87.5% in mice, outperforming other neural network and feature-based algorithms in our multimodal dataset.

Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data.

BACKGROUND: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming, invasive and often suffers from low inter- and intra-rater reliability. Therefore, an automated sleep state classification method that operates on spatiotemporal WFCI data is desired. NEW METHOD: A hybrid network architecture consisting of a convolutional neural network (CNN) to extract spatial features of image frames and a bidirectional long short-term memory network (BiLSTM) with attention mechanism to identify temporal dependencies among different time points was proposed to classify WFCI data into states of wakefulness, NREM and REM sleep. RESULTS: Sleep states were classified with an accuracy of 84 % and Cohen's κ of 0.64. Gradient-weighted class activation maps revealed that the frontal region of the cortex carries more importance when classifying WFCI data into NREM sleep while posterior area contributes most to the identification of wakefulness. The attention scores indicated that the proposed network focuses on short- and long-range temporal dependency in a state-specific manner. COMPARISON WITH EXISTING METHOD: On a held out, repeated 3-hour WFCI recording, the CNN-BiLSTM achieved a κ of 0.67, comparable to a κ of 0.65 corresponding to the human EEG/EMG-based scoring. CONCLUSIONS: The CNN-BiLSTM effectively classifies sleep states from spatiotemporal WFCI data and will enable broader application of WFCI in sleep research.

LANMAO sleep recorder versus polysomnography in neonatal EEG recording and sleep analysis.

BACKGROUND: The field of neonatal sleep analysis is burgeoning with devices that purport to offer alternatives to polysomnography (PSG) for monitoring sleep patterns. However, the majority of these devices are limited in their capacity, typically only distinguishing between sleep and wakefulness. This study aims to assess the efficacy of a novel wearable electroencephalographic (EEG) device, the LANMAO Sleep Recorder, in capturing EEG data and analyzing sleep stages, and to compare its performance against the established PSG standard. METHODS: The study involved concurrent sleep monitoring of 34 neonates using both PSG and the LANMAO device. Initially, the study verified the consistency of raw EEG signals captured by the LANMAO device, employing relative spectral power analysis and Pearson correlation coefficients (PCC) for validation. Subsequently, the LANMAO device's integrated automated sleep staging algorithm was evaluated by comparing its output with expert-generated sleep stage classifications. RESULTS: Analysis revealed that the PCC between the relative spectral powers of various frequency bands during different sleep stages ranged from 0.28 to 0.48. Specifically, the correlation for delta waves was recorded at 0.28. The automated sleep staging algorithm of the LANMAO device demonstrated an overall accuracy of 79.60 %, Cohen kappa of 0.65, and F1 Score of 76.93 %. Individual accuracy for Wake at 87.20 %, NREM at 85.70 %, and REM Sleep at 81.30 %. CONCLUSION: While the LANMAO Sleep Recorder's automated sleep staging algorithm necessitates further refinement, the device shows promise in accurately recording neonatal EEG during sleep. Its potential for minimal invasiveness makes it an appealing option for monitoring sleep conditions in newborns, suggesting a novel approach in the field of neonatal sleep analysis.

Spindle Detection Based on Elastic Time Window and Spatial Pyramid Pooling.

BACKGROUND: Sleep spindles have emerged as valuable biomarkers for assessing cognitive abilities and related disorders, underscoring the importance of their detection in clinical research. However, template matching-based algorithms using fixed templates may not be able to fully adapt to spindles of different durations. Moreover, inspired by the multiscale feature extraction of images, the use of multiscale feature extraction methods can be used to better adapt to spindles of different frequencies and durations. METHODS: Therefore, this study proposes a novel automatic spindle detection algorithm based on elastic time windows and spatial pyramid pooling (SPP) for extracting multiscale features. The algorithm utilizes elastic time windows to segment electroencephalogram (EEG) signals, enabling the extraction of features across multiple scales. This approach accommodates significant variations in spindle duration and polarization positioning during different EEG epochs. Additionally, spatial pyramid pooling is integrated into a depthwise separable convolutional (DSC) network to perform multiscale pooling on the segmented spindle signal features at different scales. RESULTS: Compared with existing template matching algorithms, this algorithm's spindle wave polarization positioning is more consistent with the real situation. Experimental results conducted on the public dataset DREAMS show that the average accuracy of this algorithm reaches 95.75%, with an average negative predictive value (NPV) of 96.55%, indicating its advanced performance. CONCLUSIONS: The effectiveness of each module was verified through thorough ablation experiments. More importantly, the algorithm shows strong robustness when faced with changes in different experimental subjects. This feature makes the algorithm more accurate at identifying sleep spindles and is expected to help experts automatically detect spindles in sleep EEG signals, reduce the workload and time of manual detection, and improve efficiency.

Sleep architecture in idiopathic hypersomnia: the influence of age, sex, and body mass index.

This study aimed to progress the understanding of idiopathic hypersomnia (IH) by assessing the moderating influence of individual characteristics, such as age, sex, and body mass index (BMI) on sleep architecture. In this retrospective study, 76 IH participants (38.1 ± 11.3 years; 40 women) underwent a clinical interview, an in-laboratory polysomnography with a maximal 9-h time in bed and a multiple sleep latency test (MSLT). They were compared to 106 healthy controls (38.1 ± 14.1 years; 60 women). Multiple regressions were used to assess moderating influence of age, sex, and BMI on sleep variables. We used correlations to assess whether sleep variables were associated with Epworth Sleepiness Scale scores and mean sleep onset latency on the MSLT in IH participants. Compared to controls, IH participants had shorter sleep latency (p = 0.002), longer total sleep time (p < 0.001), more time spent in N2 sleep (p = 0.008), and showed trends for a higher sleep efficiency (p = 0.023) and more time spent in rapid eye movement (REM) sleep (p = 0.022). No significant moderating influence of age, sex, or BMI was found. More severe self-reported sleepiness in IH patients was correlated with shorter REM sleep latency and less N1 sleep in terms of proportion and duration (ps < 0.01). This study shows that, when compared to healthy controls, patients with IH had no anomalies in their sleep architecture that can explain their excessive daytime sleepiness. Moreover, there is no moderating influence of age, sex, and BMI, suggesting that the absence of major group differences is relatively robust.