Detection Of Sleep Disorders Research Articles

Development of an IoT-based sleep pattern monitoring system for sleep disorder detection

Development of an IoT-based sleep pattern monitoring system for sleep disorder detection

Twistable and Stretchable Nasal Patch for Monitoring Sleep-Related Breathing Disorders Based on a Stacking Ensemble Learning Model.

Obstructive sleep apnea syndrome disrupts sleep, destroys the homeostasis of biological systems such as metabolism and the immune system, and reduces learning ability and memory. The existing polysomnography used to measure sleep disorders is executed in an unfamiliar environment, which may result in sleep patterns that are different from usual, reducing accuracy. This study reports a machine learning-based personalized twistable patch system that can simply measure obstructive sleep apnea syndrome in daily life. The stretchable patch attaches directly to the nose in an integrated form factor, detecting sleep-disordered breathing by simultaneously sensing microscopic vibrations and airflow in the nasal cavity and paranasal sinuses. The highly sensitive multichannel patch, which can detect airflow at the level of 0.1 m/s, has flexibility via a unique slit pattern and fabric layer. It has linearity with an R2 of 0.992 in the case of the amount of change according to its curvature. The stacking ensemble learning model predicted the degree of sleep-disordered breathing with an accuracy of 92.9%. The approach demonstrates high sleep disorder detection capacity and proactive visual notification. It is expected to help as a diagnostic platform for the early detection of chronic diseases such as cerebrovascular disease and diabetes.

Analysis of Heartbeat Signals to Detect Sleep Disorders Using Artificial Neural Network Methods

A human sleep disorder detection system has been designed using an AD8232 Electrocardiogram sensor module integrated with a microcontroller and internet connection through ESP 32. The heartbeat signals from the sensor are analyzed using Artificial Neural Network (ANN) methods to determine normal conditions, Obstructive Sleep Apnea (OSA), or Central Sleep Apnea (CSA). The sensor's accuracy was measured over 10 measurements, resulting in 96.85%. Testing with 30 training data samples achieved an accuracy of 93.33%, and testing with 20 training data samples achieved an accuracy of 80%. The system displays output values through the Internet of Things (IoT) with an average computation time of around 7.6 ms.

DCNet: A Self-Supervised EEG Classification Framework for Improving Cognitive Computing-Enabled Smart Healthcare.

Cognitive computing endeavors to construct models that emulate brain functions, which can be explored through electroencephalography (EEG). Developing precise and robust EEG classification models is crucial for advancing cognitive computing. Despite the high accuracy of supervised EEG classification models, they are constrained by labor-intensive annotations and poor generalization. Self-supervised models address these issues but encounter difficulties in matching the accuracy of supervised learning. Three challenges persist: 1) capturing temporal dependencies in EEG; 2) adapting loss functions to describe feature similarities in self-supervised models; and 3) addressing the prevalent issue of data imbalance in EEG. This study introduces the DreamCatcher Network (DCNet), a self-supervised EEG classification framework with a two-stage training strategy. The first stage extracts robust representations through contrastive learning, and the second stage transfers the representation encoder to a supervised EEG classification task. DCNet utilizes time-series contrastive learning to autonomously construct representations that comprehensively capture temporal correlations. A novel loss function, SelfDreamCatcherLoss, is proposed to evaluate the similarities between these representations and enhance the performance of DCNet. Additionally, two data augmentation methods are integrated to alleviate class imbalances. Extensive experiments show the superiority of DCNet over the current state-of-the-art models, achieving high accuracy on both the Sleep-EDF and HAR datasets. It holds substantial promise for revolutionizing sleep disorder detection and expediting the development of advanced healthcare systems driven by cognitive computing.

Sleep disorders and diabetes mellitus. Literature review and own data

The article presents analysis of current research on sleep disorders in patients with type 2 diabetes mellitus (T2DM). The mechanisms underlying the relationship between insomnia and glycemic control are complex, as both DM can cause sleep disorders and insomnia can worsen the course of diabetes. It is well known that both short (< 6 hours) and long (> 8 hours) sleep durations are associated with an elevated risk of diabetes development. The reported prevalence of sleep disorders in diabetes patients ranges between 28.0% and 59.1%. Our investigation performed in Lviv with the use of Pittsburgh Sleep Quality Index (PSQI) questionnaire, identified that 46.2% of T2DM patients experienced sleep disorders. A comprehensive literature review also unveils association between DM and diverse sleep disorders such as insomnia, circadian rhythm disturbances, obstructive sleep apnea (OSA), and restless legs syndrome (RLS). The article furnishes insights into the classification, diagnostic principles, and treatment modalities for these disorders. Referral of patients to sleep specialists for the management of sleep disorders and the integration of sleep hygiene into diabetes care can effectively mitigate symptoms, forestall DM complications, and enhance patients’ quality of life. Circadian molecules like nobiletin, REV‑ERB agonists, and CRY stabilizers have demonstrated efficacy in preclinical studies and hold potential for the development of novel treatments for conditions associated with circadian dysregulation. Considering the frequency of detection of sleep disorders in T2DM patients according to the modern literary sources and own research, the study of this problem is important for the development of an algorithm for the examination of such patients, namely the need for endocrinologists to pay attention to the sleep status of patients and, if necessary, to refer them to somnologists. Further investigation into the etiology of sleep disorders in T2DM patients and their correlation with carbohydrate metabolism indicators exhibits promise. It is imperative to explore avenues to address these causes by complementing the primary therapy for T2DM.

A multimodal attention-fusion convolutional neural network for automatic detection of sleep disorders

A multimodal attention-fusion convolutional neural network for automatic detection of sleep disorders

Automatic sleep staging and detection of sleep disorders through wearable EEG monitoring devices

Automatic sleep staging and detection of sleep disorders through wearable EEG monitoring devices

Prediction of Heart Attack Risk and Detection of Sleep Disorders Using Deep Learning Approach

This study presents a novel deep learning-based approach for predicting heart attack risk and detecting sleep disorders. Traditional models often fall short in accessibility and accuracy, leading to substandard outcomes. Our proposed ensemble-based AI model overcomes these limitations by leveraging the strengths of deep learning techniques, including neural networks, for robust analysis of sleep patterns and heart health indicators. Our model is capable of integrating data from diverse sources including wearable devices and health records. Emphasis is placed on interpretability, enabling users to comprehend predictions and take informed actions to improve their well-being. Through extensive evaluation and validation, our model demonstrates superior performance in accurately predicting heart attack risk and identifying various sleep disorders. In This research paper we have focused on advancing preventive healthcare strategies by enabling early detection and intervention, ultimately enhancing patient outcomes and healthcare resource utilization.

Developing a deep learning model for sleep stage prediction in obstructive sleep apnea cohort using 60 GHz frequency-modulated continuous-wave radar.

Given the significant impact of sleep on overall health, radar technology offers a promising, non-invasive, and cost-effective avenue for the early detection of sleep disorders, even prior to relying on polysomnography (PSG)-based classification. In this study, we employed an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model to accurately predict sleep stages using 60 GHz frequency-modulated continuous-wave (FMCW) radar. Our dataset comprised 78 participants from an ongoing obstructive sleep apnea (OSA) cohort, recruited between July 2021 and November 2022, who underwent overnight polysomnography alongside radar sensor monitoring. The dataset encompasses comprehensive polysomnography recordings, spanning both sleep and wakefulness states. The predictions achieved a Cohen's kappa coefficient of 0.746 and an overall accuracy of 85.2% in classifying wakefulness, rapid-eye-movement (REM) sleep, and non-REM (NREM) sleep (N1 + N2 + N3). The results demonstrated that the models incorporating both Radar 1 and Radar 2 data consistently outperformed those using only Radar 1 data, indicating the potential benefits of utilising multiple radars for sleep stage classification. Although the performance of the models tended to decline with increasing OSA severity, the addition of Radar 2 data notably improved the classification accuracy. These findings demonstrate the potential of radar technology as a valuable screening tool for sleep stage classification.

SLEEP-SEE-THROUGH: Explainable Deep Learning for Sleep Event Detection and Quantification From Wearable Somnography.

Evidence is rapidly accumulating that multifactorial nocturnal monitoring, through the coupling of wearable devices and deep learning, may be disruptive for early diagnosis and assessment of sleep disorders. In this work, optical, differential air-pressure and acceleration signals, acquired by a chest-worn sensor, are elaborated into five somnographic-like signals, which are then used to feed a deep network. This addresses a three-fold classification problem to predict the overall signal quality (normal, corrupted), three breathing-related patterns (normal, apnea, irregular) and three sleep-related patterns (normal, snoring, noise). In order to promote explainability, the developed architecture generates additional information in the form of qualitative (saliency maps) and quantitative (confidence indices) data, which helps to improve the interpretation of the predictions. Twenty healthy subjects enrolled in this study were monitored overnight for approximately ten hours during sleep. Somnographic-like signals were manually labeled according to the three class sets to build the training dataset. Both record- and subject-wise analyses were performed to evaluate the prediction performance and the coherence of the results. The network was accurate (0.96) in distinguishing normal from corrupted signals. Breathing patterns were predicted with higher accuracy (0.93) than sleep patterns (0.76). The prediction of irregular breathing was less accurate (0.88) than that of apnea (0.97). In the sleep pattern set, the distinction between snoring (0.73) and noise events (0.61) was less effective. The confidence index associated with the prediction allowed us to elucidate ambiguous predictions better. The saliency map analysis provided useful insights to relate predictions to the input signal content. While preliminary, this work supported the recent perspective on the use of deep learning to detect particular sleep events in multiple somnographic signals, thus representing a step towards bringing the use of AI-based tools for sleep disorder detection incrementally closer to clinical translation.

Deep convolutional architecture-based hybrid learning for sleep arousal events detection through single-lead EEG signals.

Detecting arousal events during sleep is a challenging, time-consuming, and costly process that requires neurology knowledge. Even though similar automated systems detect sleep stages exclusively, early detection of sleep events can assist in identifying neuropathology progression. An efficient hybrid deep learning method to identify and evaluate arousal events is presented in this paper using only single-lead electroencephalography (EEG) signals for the first time. Using the proposed architecture, which incorporates Inception-ResNet-v2 learning transfer models and optimized support vector machine (SVM) with the radial basis function (RBF) kernel, it is possible to classify with a minimum error level of less than 8%. In addition to maintaining accuracy, the Inception module and ResNet have led to significant reductions in computational complexity for the detection of arousal events in EEG signals. Moreover, in order to improve the classification performance of the SVM, the grey wolf algorithm (GWO) has optimized its kernel parameters. This method has been validated using pre-processed samples from the 2018 Challenge Physiobank sleep dataset. In addition to reducing computational complexity, the results of this method show that different parts of feature extraction and classification are effective at identifying sleep disorders. The proposed model detects sleep arousal events with an average accuracy of 93.82%. With the lead present in the identification, the method becomes less aggressive in recording people's EEG signals. According to this study, the suggested strategy is effective in detecting arousals in sleep disorder clinical trials and may be used in sleep disorder detection clinics.

Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning.

Sleep scoring involves the inspection of multimodal recordings of sleep data to detect potential sleep disorders. Given that symptoms of sleep disorders may be correlated with specific sleep stages, the diagnosis is typically supported by the simultaneous identification of a sleep stage and a sleep disorder. This paper investigates the automatic recognition of sleep stages and disorders from multimodal sensory data (EEG, ECG, and EMG). We propose a new distributed multimodal and multilabel decision-making system (MML-DMS). It comprises several interconnected classifier modules, including deep convolutional neural networks (CNNs) and shallow perceptron neural networks (NNs). Each module works with a different data modality and data label. The flow of information between the MML-DMS modules provides the final identification of the sleep stage and sleep disorder. We show that the fused multilabel and multimodal method improves the diagnostic performance compared to single-label and single-modality approaches. We tested the proposed MML-DMS on the PhysioNet CAP Sleep Database, with VGG16 CNN structures, achieving an average classification accuracy of 94.34% and F1 score of 0.92 for sleep stage detection (six stages) and an average classification accuracy of 99.09% and F1 score of 0.99 for sleep disorder detection (eight disorders). A comparison with related studies indicates that the proposed approach significantly improves upon the existing state-of-the-art approaches.

Connection of individual typological features of personality with nature of dreams

Objective. To study the influence of the temperament, anxiety level, intro-extraversion index, the nature of emotions, the level of happiness and the presence of pathologies of sleep physiology in the individual on the frequency of dream production and their figurative content.
 Materials and methods. The research involved 100 respondents students of E.A. Vagner Perm State Medical University, the average age was 22.5 4.5 years. The study was conducted through anonymous questionnaires using the following personality questionnaires and psychological tests: Eysenck Personality Questionnaire, Comprehensive Sociometric Test: Emotions, Oxford Happiness Questionnaire (OHQ), Comprehensive Test for Detection of Sleep Disorders.
 Results. It was found that in 37,7 % of cases out of 100,0 %, the imaginative content of dreams and the frequency of their production directly depend on the temperament of the individual, the level of anxiety affects dreams only in 50,0 % of cases out of 100,0 %. The intro-extraversion index has a significant impact on the nature of dreams and manifests its effect in 61,6 % out of 100,0 %. The complete dependence of the imaginative content of dreams on the quality of emotions is observed in 57.0 % of cases; inversely proportional dependence is noted in 43.0 % of cases. It was determined that the level of happiness has a natural effect on dreams in 66.0 % of cases. Sleep disorders can affect both negatively and positively the imaginative content of dreams and their quantitative production. Assessment of the multifactorial influence of the students' psycho-emotional state showed that the most important role in the cumulative effect on sleep and dreams is played by the level of anxiety and temperament.
 Conclusions. Changes in the parameters of dreams are a consequence of the multifactorial interaction of individual psychoemotional characteristics and typological characteristics of the personality. Determination of temperament and registration of the level of anxiety of the individual can contribute to the prediction of the overall picture of dreams for use in psychological and psychiatric practice.

О важности своевременного выявления нарушений сна как независимого фактора риска развития сердечно-сосудистых заболеваний

О важности своевременного выявления нарушений сна как независимого фактора риска развития сердечно-сосудистых заболеваний

Prevalence of Sleep Hygiene Awareness, Practices and Quality among Elementary School Students in Canada

Enough sleep is crucial for physical and emotional growth of kids and students. It has been noted that there is a progressive decline in bedtime as kids turn into teens, despite the fact that the need for sleep among students does not decrease. Teenagers need for sleep has been estimated as much as 9.2 h per day. Poor sleep has actually been noted to lead to poor diet regimen, excessive weight, stunted growth, psychological health concerns, as well as chemical abuse. Much as the need for sleep is an individual particular, approximately at least 8.3 hrs. of rest per evening is recommended for teens, to avoid excessive daytime drowsiness. High quality sleep is necessary for young professional athletes for making sure much better performance in psychomotor as well as cognitive activities, and for minimizing the chances of creating challenging factors for musculoskeletal discomfort. Detection of sleep disorders in adolescent complaining of sleeplessness is a day-to-day challenge for doctors and clients alike. The purpose of the research study was to assess the prevalence of sleep hygiene awareness, practices and quality among elementary schools students in Canada. The study was a literature based. The research discovered that a boost in the occurrence of difficulty in getting sleep and in too much screen time exposure and marginal increase in physical activity levels. In addition, teens exceeding 2-h everyday display time had 20% higher probabilities of reporting sleep-onset problems, while no association was discovered for physical activity. The study also discovered that a regular every night routine aids the body recognize that it is going to bed; this could consist of taking warm shower or bathroom, checking out a publication, or light stretches. Whenever feasible it is essential for the students to try to avoid emotionally distressing conversations and activities before attempting to sleep considering that maintaining a routine sleep timetable also on weekends keeps the timing of the body's body clock and also can aid you fall asleep and wake up much more quickly. Based on the finding, the research study concluded that the daily substances that students take, the medicines one takes, how long they sleep in a day and exactly how they choose to invest their nights can considerably influence their quality of sleep. Also a couple of mild adjustments can, in many cases indicate the difference between sound rest as well as a restless evening. Finishing a two-week rest diary can help one understand how their routines impact their sleep. The study suggested that teenagers must reserve no more than eight hrs. For sleep given that the suggested quantity of sleep for a healthy grownup goes to least 7 hrs. Keywords: Sleep Hygiene, Awareness, Sleep Quality, Sleep Disorder, Students

Supervised approach based sleep disorder detection using non - Linear dynamic features (NLDF) of EEG

Depending on its intensity, sleep disorders can affect a person's ability to function mentally, emotionally, and physically. These are medical abnormalities of the subject's sleep structure. Most prevalent ones are bruxism, sleep problems, depression, and narcolepsy. A greater chance of acquiring sleep issues in the elderly includes sleeplessness, irregular leg movements, problems with fast eye movement behaviour and breathing abnormalities. Therefore, an early stage therapy that might save a patient's life depends on a precise diagnosis and categorization. The much more sensitive as well as significant bio-signal is electroencephalographic (EEG) signal. It has the capacity to record sleep-sensitive brain activity. We used an available EEG database which had recordings divided into different types of sleep disturbances as well as a healthy control group. Popular sensor's EEG brain function has been examined. Ultimately, using patterns taken from EEG data, a categorization AI model was created. Extracted characteristics worked well as a biomarker for identifying sleep problems when combined with an AI classifier.

Breath monitoring, sleep disorder detection, and tracking using thin-film acoustic waves and open-source electronics

Apnoea, a major sleep disorder, affects many adults and causes several issues, such as fatigue, high blood pressure, liver conditions, increased risk of type II diabetes, and heart problems. Therefore, advanced monitoring and diagnosing tools of apnoea disorders are needed to facilitate better treatment, with advantages such as accuracy, comfort of use, cost effectiveness, and embedded computation capabilities to recognise, store, process, and transmit time series data. In this work we present an adaptation of our apnoea-Pi open-source surface acoustic wave (SAW) platform (Apnoea-Pi) to monitor and recognise apnoea in patients. The platform is based on a thin-film SAW device using bimorph ZnO and Al structures, including those fabricated as Al foils or plates, to achieve breath tracking based on humidity and temperature changes. We applied open-source electronics and provided embedded computing characteristics for signal processing, data recognition, storage, and transmission of breath signals. We show that the thin-film SAW device out-performed standard and off-the-shelf capacitive electronic sensors in terms of their response and accuracy for human breath-tracking purposes. This in combination with embedded electronics makes a suitable platform for human breath monitoring and sleep disorder recognition.

Anxiety and sleep disorders

Anxiety disorders are widespread among patients of primary care physicians and are the cause of increased morbidity and access to healthcare. Anxiety is a reaction of the body to the effects of stress and is manifested by the development of psychovegetative syndrome. In the general population, 5 to 7% of people have clinically significant anxiety, and in the practice of primary care physicians, these disorders are detected in every fourth patient. Anxiety disorder contributes to the development of psychogenic somatic pathology and/or negatively affects the course of background disease. Most patients with anxiety disorders suffer from various sleep disorders. In general practice, the frequency of sleep disorders reaches 73%. Chronic sleep disorder can act as a stressor that contributes to the development of pathophysiological changes in the body and increases anxiety. The above determines the need for early detection of sleep disorders and its timely treatment. The current treatment strategy for patients with sleep disorders involves an individualized approach to insomnia therapy. The existing arsenal of drugs used to treat sleep disorders allows this principle to be applied taking into account concomitant diseases (including anxiety disorders). The choice of product is also determined by good tolerability, efficacy and high safety profile. The optimal drug that answers the above requirements is doxylamine succinate, which allows you to effectively stop both anxiety symptoms and sleep disorders. Due to its high safety profile, doxylamine succinate can be used in the treatment of insomnia in pregnant women. The release form determines the convenience of use and the possibility of selecting an individual dose of the drug.

CMS2-Net: Semi-Supervised Sleep Staging for Diverse Obstructive Sleep Apnea Severity.

Although the development of computer-aided algorithms for sleep staging is integrated into automatic detection of sleep disorders, most supervised deep learning-based models might suffer from insufficient labeled data. While the adoption of semi-supervised learning (SSL) can mitigate the issue, the SSL models are still limited to the lack of discriminative feature extraction for diverse obstructive sleep apnea (OSA) severity. This model deterioration might be exacerbated during the domain adaptation. Such exploration on the alleviation of domain-shift of SSL model between different OSA conditions has attracted more and more attentions from the clinic. In this work, a co-attention meta sleep staging network (CMS2-net) is proposed to simultaneously deal with two issues: the inter-class disparity problem and the intra-class selection problem. Within CMS2-net, a co-attention module and a triple-classifier are designed to explicitly refine the coarse feature representations by identifying the class boundary inconsistency. Moreover, the mutual information with meta contrastive variance is introduced to supervise the gradient stream from a multi-scale view. The performance of the proposed framework is demonstrated on both public and local datasets. Furthermore, our approach achieves the state-of-the-art SSL results on both datasets.

Altered Motor Performance, Sleep EEG, and Parkinson's Disease Pathology Induced by Chronic Sleep Deprivation in Lrrk2G2019S Mice.

Parkinson's disease (PD) is a multifaceted disease in which environmental variables combined with genetic predisposition cause dopaminergic (DAergic) neuron loss in the substantia nigra pars compacta. The mutation of leucine-rich repeat kinase 2 (Lrrk2) is the most common autosomal dominant mutation in PD, and it has also been reported in sporadic cases. A growing body of research suggests that circadian rhythm disruption, particularly sleep-wake abnormality, is common during the early phase of PD. Our present study aimed to evaluate the impact of sleep deprivation (SD) on motor ability, sleep performance, and PD pathologies in Lrrk2G2019S transgenic mice. After two months of SD, Lrrk2G2019S mice at 12 months of age showed an exacerbated PD-like phenotype with motor deficits, a reduced striatal DA level, degenerated DAergic neurons, and altered sleep structure and biological rhythm accompanied by the decreased protein expression level of circadian locomotor output cycles kaput Lrrk2 gene in the brain. All these changes persisted and were even more evident in 18-month-old mice after 6 months of follow-up. Moreover, a significant increase in α-synuclein aggregation was found in SD-treated transgenic mice at 18 months of age. Taken together, our findings indicate that sleep abnormalities, as a risk factor, may contribute to the pathogenesis and progression of PD. Early detection of sleep disorders and improvement of sleep quality may help to delay disease progression and provide long-term clinical benefits.