Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder.

Accurate detection of sleep stages and sleep events is essential for the effective diagnosis and treatment of sleep disorders. However, current state-of-the-art methods often fall short in integrating these multiple tasks simultaneously. This study introduces a novel multi-task deep-learning approach for the joint detection of sleep events and hypnogram construction in a single pass. The proposed method adapts state-of-the-art single-shot object detection techniques to multi-channel time-series data, enabling simultaneous classification and detection of sleeping events. Experiments were conducted using diverse input channel combinations, including electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), oxygen saturation, airflow, and thoracic-abdominal movements, as well as multiple output configurations regarding sleep stages, EEG arousals, and respiratory events, both individually and combined. Results demonstrate improved accuracy in detecting respiratory events and sleep stages with additional channels. The multi-task approach enhanced overall performance, benefiting from shared knowledge across tasks.

Diagnosis of sleep disorders in optimized time slot based machine learning techniques on EEG energy level patterns

To evaluate the effects of adjunctive pregabalin 300 mg/day versus placebo on polysomnographic (PSG) variables in patients with well controlled partial seizures and subjectively reported sleep disturbance. An exploratory, 4-week, double-blind, randomized study in patients with well controlled partial seizures on AED monotherapy and subjective sleep disturbance over the previous 6 months. Mean changes from baseline to endpoint in PSG and subjective sleep variables (MOS Sleep Scale, Groningen Sleep Questionnaire) in patients on adjunctive pregabalin 300 mg/day (n=8) were compared with patients on placebo (n=7). Baseline PSGs showed sleep fragmentation. Mean sleep efficiency improved significantly in both treatment groups in the mean baseline to endpoint change; there was no significant between-group difference. Pregabalin treatment was associated with a significant reduction in number of awakenings (p = 0.02), and improvement in wake time after sleep onset approached significance (p = 0.055), suggesting improvement in sleep continuity that was not observed in the placebo group. Pregabalin was also associated with significant improvements in the MOS sleep disturbance and sleep quantity subscales compared with placebo (p < or =0.03). There were no changes in self-reported seizure control. This exploratory pilot study suggests that pregabalin may improve sleep continuity in patients with clinically relevant sleep disturbance. The effect on disturbed sleep appears independent of seizure control. The effects of pregabalin on disturbed sleep and seizures and their interrelationships warrant further study.

Comfortable and reliable wearable systems for home-based automatic sleep staging are crucial for remotely diagnosing sleep disorders. Among various categories of biosignals, electrooculography (EOG) has emerged as a promising modality due to its ease of acquisition and robustness to noise. However, research on EOG-based sleep staging remains limited, with most studies constrained to the standard 2-channel EOG configuration recommended by the American Academy of Sleep Medicine (AASM) scoring manual. Thus, it may lack higher spatial information, hindering fully capturing the tiny sleep-related eye movements. In this study, we developed a novel eye mask-based EOG acquisition system capable of collecting 14-channel spatially diverse EOG signals. Building upon this hardware innovation, we further proposed a dedicated deep learning model, MTNet, combining multi-scale feature extraction and temporal attention, to fully exploit the rich information from the collected EOG data for accurate sleep staging , which is capable of effectively capturing relevant sleep features from EOG signals. We also evaluated the importance of each EOG channel for sleep staging, providing recommendations for EOG-based data acquisition in-home sleep monitoring systems. Our study involved 24 participants, resulting in 48 overnight sleep recordings. In K-fold cross-validation, 14-channel EOG achieved an average accuracy of 83.2%, a Cohen’s kappa coefficient of 0.77, and an MF1 score of 77.7%. These results surpass the performance of the standard EOG, which yielded an accuracy of 82.3%, a Cohen’s kappa coefficient of 0.76, and an MF1 score of 76.1%. In particular, the 14-channel EOG demonstrated superior detection accuracy in the N1 and REM sleep stages compared to the standard setup. In conclusion, the proposed 14-channel EOG collection system—formed as a wearable eye mask—shows significant potential as a high-dimensional biosignal source for home-based sleep monitoring. By combining 14-channel EOG acquisition with the MTNet model, and investigating the importance of channels, this study marks a substantial step forward in an accurate and user-friendly sleep monitoring solution.

Sleep stages classification by fusing the time-related synchronization analysis and brain activations

Sleep comprises one third of human life and is very important for maintaining healthy mental and physical states. Sleep disorders disturbing normal sleep are very common and may induce serious consequences. Sleep disorder medicine is based on the concept of two different states (waking and sleep) of the human brain. The problems of the waking conscious state affect sleep and problems during sleep also affect daytime mental and physical activities. The common symp-toms of patients with sleep disorders are excessive daytime sleepiness, a decrease in alertness, and fatigue. To detect sleep disorders, we should understand the physiology of normal sleep. Normal sleep consists of non-rapid eye movement (75% to 80%) and rapid eye movement (20% to 25%) sleep. The International Classification of Sleep Disorders second edition lists 77 different sleep disorders divided into 8 categories. Clinical approaches to sleep disorder patients should include detailed history taking including sleep history, a sleep questionnaire, sleep diary, physical examination including the nasal/oral cavities and airway, and neurological/psychological examination. The common sleep disorders are obstructive sleep apnea, insomnia, narcolepsy, restless legs syndrome, rapid eye movement sleep behavior disorder, and circadian rhythm sleep disorder. We should learn the characteristic clinical features of each sleep disorder and how to detect and treat them. We need a more active effort to educate physicians about sleep disorder medicine and should try large sample, long-term, and prospective studiesto reveal the pathophysiology and enhance the treatment of sleep disorders. Sleep disorders are common, serious, and treatable. However, most patients with sleep disorders are underdiagnosed and not treated appropriately due to lack of knowledge of sleep disorder medicine. The Ministry of Health and Welfare of South Korea should focus more attention on and provide more support for timely diagnosis and treatment of sleep disorders.

Cardiovascular Disease and Sleep Disordered Breathing: Are Children Vulnerable?

Sedative and hypnotic medications are associated with harm, and guidelines suggest limiting their use. Only limited evidence has described how older adults are managed following an initial sleep disorder diagnosis. We aimed to describe clinical management patterns of sleep disorders in older women and men at the time of initial diagnosis. Population-based retrospective cohort study using linked administrative databases. Ontario, Canada. Community-dwelling adults aged 66 and older, diagnosed with a new sleep disorder by a primary care provider (n = 30 729; 56% women and 44% men). We compared women and men for each outcome. The primary outcome was prescription of a medication used for sleep within 30 days of a new sleep disorder diagnosis. Additional analysis included medical investigations such as sleep studies and visits to specialists who manage obstructive sleep disorders within 90 days of diagnosis. Among the 30 729 older adults with a new sleep disorder diagnosis, 5512 (17.9% total; 18.8% of women and 16.9% of men) were prescribed a medication used for sleep. Compared with men, women were somewhat more likely to be prescribed at least one sedative medication (adjusted odds ratio = 1.09; 95% confidence interval = 1.03-1.16). A total of 2573 (8.4%) older adults underwent a sleep study, and 3743 (12.2%) were evaluated by a specialist; both occurred more commonly in men. In our cohort, almost 1 in 5 older adults with a new sleep disorder diagnosis were prescribed a medication used for sleep; of these, a higher proportion were women. Comparatively few older adults were further evaluated; of these, a higher proportion were men. Our study highlights the high rates at which medications are prescribed to older adults with a new sleep disorder diagnosis and identifies potential sex differences in the management of such diagnoses. J Am Geriatr Soc 1-8, 2019. J Am Geriatr Soc 67:2094-2101, 2019.

What is the central question of this study? A transcranial magnetic stimulation (TMS)-induced twitch applied on isolated single breaths over the motor cortex somatotopic representation of the tongue briefly recruits submental muscles and improves airflow dynamics of flow-limited respiratory cycles without arousing sleep apnoea patients. However, the mechanical impact of the TMS-induced twitch applied during consecutive breathing cycles on airflow dynamics remains unknown. What is the main finding and what is its importance? Our results show that application of TMS with the stimulator output set at the sleep submental motor threshold intensity on consecutive respiratory cycles increases inspiratory flow and reduces the turbulent airflow component. These results indicate an improvement of airflow pattern after two single consecutive TMS-induced twitches without arousing sleep apnoea patients. Transcranial magnetic stimulation (TMS)-induced twitches applied on isolated breaths briefly recruit upper airway dilator muscles and improve airflow and inspiratory volume without arousing apnoeic patients from sleep, but the effects of applying such twitches consecutively on airflow dynamics is unknown. The objective of this study was to quantify the effects of five consecutive TMS-induced twitches applied on sleep-induced obstructive hypopnoeic breaths in 10 obstructive sleep apnoea patients. Submental muscle motor threshold (SUBMT) and motor-evoked potential were measured during wakefulness and sleep. The TMS-induced twitches were applied during stable non-rapid eye movement (NREM) sleep, at the beginning of inspiration of consecutive flow-limited respiratory cycles, with the stimulator output set at sleep SUBMT. Maximal inspiratory flow, inspiratory volume, inspiratory time, shifts of electroencephalogram frequency and pulse rate variability were assessed. During sleep, SUBMT increased (wakefulness, 25.3 ± 4.9%; NREM sleep, 27.0 ± 6.2%; P = 0.02). During each series of stimulations there was a rise in maximal inspiratory flow (from 306.7 ± 123.2 to 359.8 ± 154.1 ml s(-1); P = 0.0002) and in inspiratory volume (from 346.1 ± 128.1 to 414.9 ± 171.2 ml; P = 0.02) without differences in thoraco-abdominal efforts and inspiratory time. These responses were observed in the absence of arousals and ceased immediately after TMS interruption. Transcranial magnetic stimulation-induced cortical and/or autonomic arousal was observed in 30.2% of all series of stimulation. Consecutive twitch TMS of submental muscles may lead to arousals in a minority of patients but can be applied on consecutive respiratory cycles during sleep and can significantly improve maximal inspiratory flow and inspiratory volume of flow-limited cycles.

Automated sleep staging is essential to assess sleep quality and treat sleep disorders, so the issue of electroencephalography (EEG)-based sleep staging has gained extensive research interests. However, the following difficulties exist in this issue: 1) how to effectively learn the intrinsic features of salient waves from single-channel EEG signals; 2) how to learn and capture the useful information of sleep stage transition rules; 3) how to address the class imbalance problem of sleep stages. To handle these problems in sleep staging, we propose a novel method named SleepFC. This method comprises convolutional feature pyramid network (CFPN), cross-scale temporal context learning (CSTCL), and class adaptive fine-tuning loss function (CAFTLF) based classification network. CFPN learns the multi-scale features from salient waves of EEG signals. CSTCL extracts the informative multi-scale transition rules between sleep stages. CAFTLF-based classification network handles the class imbalance problem. Extensive experiments on three public benchmark datasets demonstrate the superiority of SleepFC over the state-of-the-art approaches. Particularly, SleepFC has a significant performance advantage in recognizing the N1 sleep stage, which is challenging to distinguish.

Sleep quality in patients studied with laboratory-based polysomnography may differ from sleep quality in patients studied at home but remains clinically relevant and important to describe. We assessed objective sleep quality and explored factors associated with poor sleep in patients undergoing laboratory-based polysomnography. We reviewed diagnostic polysomnography studies from a 10-year period at a single sleep center. Total sleep time (TST) and sleep efficiency (SE) were assessed as markers of sleep quality. Poor sleep was defined as TST ≤ 4 hours or SE ≤ 50%. Multivariable analysis was performed to determine associations between objective sleep quality as an outcome and multiple candidate predictors including age, sex, race, body mass index, comorbidities, severity of obstructive sleep apnea, and central nervous system medications. Among 4957 patients (age 53 ± 15 years), average TST and median SE were 5.8 hours and 79%, respectively. There were 556 (11%) and 406 (8%) patients who had poor sleep based on TST and SE, respectively. In multivariable analysis, those who were older (per 10 years: 1.48 [1.34, 1.63]), male (1.38 [1.14,1.68]), and had severe obstructive sleep apnea (1.76 [1.28, 2.43]) were more likely to have short sleep. Antidepressant use was associated with lower odds of short sleep (0.77 [0.59,1.00]). Older age (per 10 years: 1.48 [1.34, 1.62]), male sex (1.34 [1.07,1.68]), and severe obstructive sleep apnea (2.16 [1.47, 3.21]) were associated with higher odds of poor SE. We describe TST and SE from a single sleep center cohort. Multiple demographic characteristics were associated with poor objective sleep in patients during laboratory-based polysomnography. Harrison EI, Roth RH, Lobo JM, et al. Sleep time and efficiency in patients undergoing laboratory-based polysomnography. J Clin Sleep Med. 2021;17(8):1591-1598.

Sleep disorders often predict or co-occur with cognitive decline. Yet, little is known about how the relationship unfolds among older adults at risk for cognitive decline. To examine the associations of sleep disorders with cognitive decline in older adults with unimpaired cognition or impaired cognition (mild cognitive impairment and dementia). A total of 5,822 participants (Mage = 70) of the National Alzheimer's Coordinating Center database with unimpaired or impaired cognition were followed for 3 subsequent waves. Four types of clinician-diagnosed sleep disorders were reported: sleep apnea, hyposomnia/insomnia, REM sleep behavior disorder, or "other." Cognition over time was measured by the Montreal Cognitive Assessment (MoCA) or an estimate of general cognitive ability (GCA) derived from scores based on 12 neuropsychological tests. Growth curve models were estimated adjusting for covariates. In participants with impaired cognition, baseline sleep apnea was related to better baseline MoCA performance (b = 0.65, 95% confidence interval [95% CI] = [0.07, 1.23]) and less decline in GCA over time (b = 0.06, 95% CI = [0.001, 0.12]). Baseline insomnia was related to better baseline MoCA (b = 1.54, 95% CI = [0.88, 2.21]) and less decline in MoCA over time (b = 0.56, 95% CI = [0.20, 0.92]). Furthermore, having more sleep disorders (across the 4 types) at baseline predicted better baseline MoCA and GCA, and less decline in MoCA and GCA over time. These results were only found in those with impaired cognition and generally consistent when using self-reported symptoms of sleep apnea or insomnia. Participants with sleep disorder diagnoses may have better access to healthcare, which may help maintain cognition through improved sleep.

Sleep restriction and emotion, electroencephalography (EEG) and dream recall, and insomnia and punctuality

Chinese agricultural diseases and pests named entity recognition with multi-scale local context features and self-attention mechanism

With the development of telemedicine and edge computing, edge artificial intelligence (AI) will become a new development trend for smart medicine. On the other hand, nearly one-third of children suffer from sleep disorders. However, all existing sleep staging methods are for adults. Therefore, we adapted edge AI to develop a lightweight automatic sleep staging method for children using single-channel EEG. The trained sleep staging model will be deployed to edge smart devices so that the sleep staging can be implemented on edge devices which will greatly save network resources and improving the performance and privacy of sleep staging application. Then the results and hypnogram will be uploaded to the cloud server for further analysis by the physicians to get sleep disease diagnosis reports and treatment opinions. We utilized 1D convolutional neural networks (1D-CNN) and long short term memory (LSTM) to build our sleep staging model, named CSleepNet. We tested the model on our childrens sleep (CS) dataset and sleep-EDFX dataset. For the CS dataset, we experimented with F4-M1 channel EEG using four different loss functions, and the logcosh performed best with overall accuracy of 83.06% and F1-score of 76.50%. We used Fpz-Cz and Pz-Oz channel EEG to train our model in Sleep-EDFX dataset, and achieved an accuracy of 86.41% without manual feature extraction. The experimental results show that our method has great potential. It not only plays an important role in sleep-related research, but also can be widely used in the classification of other time sequences physiological signals.