دانلود و نمایش مقالات مرتبط با Sleep::صفحه 2
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نتیجه جستجو - Sleep

تعداد مقالات یافته شده: 48
ردیف عنوان نوع
11 Upcoming Scenarios for the Comprehensive Management of Obstructive Sleep Apnea: An Overview of the Spanish Sleep Network
سناریوهای آینده برای مدیریت جامع Apnea خواب انسدادی: مروری بر شبکه خواب اسپانیایی-2020
Sleep is considered an essential part of life and plays a vital role in good health and well-being. Equally important as a balanced diet and adequate exercise, quality and quantity of sleep are essential for maintaining good health and quality of life. Sleep-disordered breathing is one of the most prevalent conditions that compromises the quality and duration of sleep, with obstructive sleep apnea (OSA) being the most prevalent disorder among these conditions. OSA is a chronic and highly prevalent disease that is considered to be a true public health problem. OSA has been associated with increased cardiovascular, neurocognitive, metabolic and overall mortality risks, and its management is a challenge facing the health care system. To establish the main future lines of research in sleep respiratory medicine, the Spanish Sleep Network (SSN) promoted the 1st World Café experts’ meeting. The overall vision was established by consensus as “Sleep as promoter of health and the social impact of sleep disturbances”. Under this leitmotiv and given that OSA is the most prevalent sleep disorder, five research lines were established to develop a new comprehensive approach for OSA management: (1) an integrated network for the comprehensive management of OSA; (2) the biological impact of OSA on comorbidities with high mortality, namely, cardiovascular and metabolic diseases, neurocognitive diseases and cancer; (3) Big Data Analysis for the identification of OSA phenotypes; (4) personalized medicine in OSA; and (5) OSA in children: current needs and future perspectives. Keywords: Obstructive sleep apnea | Continuous positive airway pressure | Clinical management | Personalized medicine
مقاله انگلیسی
12 Influence of a tamping operation on the vibrational characteristics and resistance-evolution law of a ballast bed
تأثیر یک عملیات تعدیل بر روی ویژگیهای ارتعاشی و قانون مقاومت در برابر تکامل بستر ballast-2020
A ballast bed provides a stable foundation and a sufficient vibration-damping performance for a railway line due to its loose and porous structure. Tamping operations change the compactness of ballast stacks and improve and maintain the state of ballast beds, which also affect the vibration-transfer characteristics and resistance performance of ballast beds. In the present study, impact excitation technology was used to test and analyze the influence of the number of tamping operations performed by a tamping car on the damping and stiffness of a ballast bed. Additionally, the influence of different tamping operations on the longitudinal vibration-transmission characteristics of the ballast bed was obtained by the methods of ‘‘single-point excitation and multi-point pick-up”. The mode of analysis of the railway structure was carried out by numerical simulation, and the resistance test on site was used to study the evolution law of the longitudinal and lateral resistance of the ballast bed during three tamping operations. The results showed that a large machine tamping operation changed the values of the two dominant resonances (140.75 Hz to 202.35 Hz and 381.25 Hz to 418.75 Hz) of track structure in the range of 0–600 Hz. The tamping operation also affected the resistance, damping, and stiffness of the ballast bed (the longitudinal and lateral resistances decreased by 17.39% and 20.34%, respectively, and the damping and stiffness increased by 204.07% and 148.80%, respectively), which suggests that the longitudinal/lateral resistances, damping, and stiffness of the ballast bed were strongly negatively correlated with one another. The modal shapes of the track structure caused by the first tamping and the second tamping were obviously different, and the positions and values of the maximum displacement changed (1.92 mm to 1.71 mm). The tamping also affected the longitudinal vibration transmission between the sleepers, and the maximum attenuation rate between adjacent sleepers was 81.25%. The change of the resistance work of the ballast bed with the displacement was more significant than the change of the resistance of the ballast bed with the displacement (when the ballast bed resistance decrement rates were 4.34%, 16.19%, and 20.34%, the track bed resistance work decrement rates were 13.14%, 28.15%, and 39.35%, respectively). Thus, the resistance work was more applicable when describing the variational degree of the stability of the ballast bed.
Keywords: High-speed railway | Ballast bed | Tamping operation | Vibration characteristics | Resistance evolution
مقاله انگلیسی
13 Origin and dynamics of cortical slow oscillations
منشا و پویایی نوسانات آهسته قشر مغز-2020
Slow oscillations are the coordinated activity of large neuronal populations consisting of alternating active (Up states) and silent periods (Down states). These oscillations occur in the corticothalamocortical network during slow-wave sleep and deep anesthesia. They also spontaneously occur in isolated cortical slices or in disconnected ‘cortical islands’ in brain damage. This rhythmic activity emerges in the cortical network when there are no other driving inputs and is considered its default activity pattern. During Up states, neocortical neurons receive barrages of synaptic inputs and fire action potentials. During Down states, neurons remain silent; rather they are hyperpolarized, and synaptic activity is almost nonexistent. From a dynamic perspective, this pattern is often referred to as a state-dependent bistability. During Up states, the activity expresses coherent oscillations at high frequencies in the beta and gamma ranges, sharing properties with wakefulness. The impact of Up/Down states on synaptic transmission and plasticity and its relationship with sleep are discussed.
مقاله انگلیسی
14 An Expert System Gap Analysis and Empirical Triangulation of Individual Differences, Interventions, and Information Technology Applications in Alertness of Railroad Workers
تجزیه و تحلیل شکاف سیستم خبره و مثلث تجربی تفاوت های فردی ، مداخلات و کاربردهای فناوری اطلاعات در هوشیاری کارگران راه‌آهن-2019
In this abstract we would like to provide some exciting concrete information including the article’s main impact and significance on expert and intelligent systems. The main impact is that the PTC expert intelligent system fills in the gaps between the human and software decision making processes. This gap analysis is analyzed via empirical triangulation of rail worker data collected from its groups, individuals and the rail industry itself. We utilize an expert intelligent system PTC information technology application to both measure and to improve the alertness of the groups and workers in order to improve the overall safety of the railways through reduced human errors and failures to prevent accidents. Many individual differences in alertness among military, railroad, and other industry workers stem from a lack of sufficient sleep. This continues to be a concern in the railroad industry, even with the implementation of positive train control (PTC) expert system technology. Information technology aids such as PTC cannot prevent all accidents, and errors and failures with PTC may occur. Furthermore, drug interventions are a short-term solution for improving alertness. This study investigated the effect of sleep deprivation on the alertness of railroad signalmen at work, individual differences in alertness, and the information technology available to improve alertness. We investigated various information and communication technology control systems that can be used to maintain operational safety in the railroad industry in the face of incompatible circadian rhythms due to irregular hours, weekend work, and night operations. To fully explain individual differences after the adoption of technology, our approach posits the necessary parameters that one must consider for reason-oriented action, sequential updating, feedback, and technology acceptance in a unified model. This triangulation can help manage workers by efficiently increasing their productivity and improving their health. In our analysis we used R statistical software and Tableau. To test our theory, we issued an Apple watch to a locomotive engineer. The perceived usefulness, perceived ease of use, and actual use he reported led to an analysis of his sleep patterns that eventually ended in his adoption of a sleep apnea device and an improvement in his alertness and effectiveness. His adoption of the technology also resulted in a decrease in his use of chemical interventions to increase his alertness. Our model shows that the alertness of signalmen can be predicted. Therefore, we recommend that the alertness of all railroad workers be predicted given the safety limitations of PTC.
Keywords : Sleep Deprivation | Fatigue | Stress | Expert System | Alertness | Empirical Analysis
مقاله انگلیسی
15 Blaming rape on sleep: A psychoanalytic intervention
سرزنش تجاوز در خواب: یک مداخله روانکاوی-2019
The governance of sleep sex (or sexsomnia) in the criminal law is a nightmare. Press reports of sleeping, often drunk, men acquitted as automatons of raping adults and children suggest cases are rising. The use of automatism, rather than insanity, in these cases is strong evidence of the immemorial struggle faced by legal psychiatry in appropriately construing unconscious defendants. This paper responds by drawing on well-established psychoanalytic conceptions of unconsciousness to present sexsomnia as dispositional to the defendant. Taking the Freudian concepts of eros and death instinct, it asserts that sexsomniacs are acting on repressed sadistic desires. Accordingly, those on notice of their sexsomnia, who fail to mitigate the risk of further attacks, should be guilty of rape. Reliance on (a reformed) insanity defence – being a denial of responsibility at the time of the offence – undermines the scope of the criminal law to self-responsibilise sexsomniacs against perpetrating unwanted sex.
Keywords: Sexsomnia | Rape | Insanity | Automatism | Freud | Repression
مقاله انگلیسی
16 A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data
بررسی سیستماتیک پردازش زبان طبیعی و استخراج متن علائم از داده های متنی نویسنده بیمار الکترونیکی-2019
Objective: In this systematic review, we aim to synthesize the literature on the use of natural language processing (NLP) and text mining as they apply to symptom extraction and processing in electronic patient-authored text (ePAT). Materials and methods: A comprehensive literature search of 1964 articles from PubMed and EMBASE was narrowed to 21 eligible articles. Data related to purpose, text source, number of users and/or posts, evaluation metrics, and quality indicators were recorded. Results: Pain (n=18) and fatigue and sleep disturbance (n=18) were the most frequently evaluated symptom clinical content categories. Studies accessed ePAT from sources such as Twitter and online community forums or patient portals focused on diseases, including diabetes, cancer, and depression. Fifteen studies used NLP as a primary methodology. Studies reported evaluation metrics including the precision, recall, and F-measure for symptom-specific research questions. Discussion: NLP and text mining have been used to extract and analyze patient-authored symptom data in a wide variety of online communities. Though there are computational challenges with accessing ePAT, the depth of information provided directly from patients offers new horizons for precision medicine, characterization of subclinical symptoms, and the creation of personal health libraries as outlined by the National Library of Medicine. Conclusion: Future research should consider the needs of patients expressed through ePAT and its relevance to symptom science. Understanding the role that ePAT plays in health communication and real-time assessment of symptoms, through the use of NLP and text mining, is critical to a patient-centered health system.
Keywords: Natural language processing | Signs and symptoms | Electronic patient-authored text | Review
مقاله انگلیسی
17 الگوریتم زمانبندی گره مبتنی بر توری برای شبکه‎های حسگر بی‌سیم
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 13
نحوه‌ی کاهش مصرف انرژی شبکه و افزایش عمر مفید شبکه‌ی حسگر بی‌سیم یکی از مباحث پژوهشی مهم در حوزه‌ی شبکه‌ی حسگر بی‌سیم است. با پیش‌فرض اطمینان از پوشش شبکه، الگوریتم زمانبندی گره که گره‎های زائد ا به حالت خواب می‌برد، اقدام کارآمدی برای کاهش مصرف انرژی است. در این مقاله، یک سازوکار زمانبندی گره مبتنی بر توری پیشنهاد شده است. این سازوکار، وزن تمامی گره‌های موجود در هر توری را محاسبه نموده و سپس مشخص می‌سازد آیا گره جزو گره‌های با پوشش زائد است یا خیر. نتیجه‎‌ی شبیه‌سازی متلب نشان می‌دهد که این سازوکار می‌توان به خوبی زمان بقای شبکه را افزایش دهد. کلیدواژه ها: شبکه‌ی حسگر بی‌سیم | مش‌بندی | زمانبندی گره | الگوریتم زمانبندی
مقاله ترجمه شده
18 Exploring the fatigue affecting electroencephalography based functional brain networks during real driving in young males
بررسی خستگی مؤثر بر شبکه های عملکردی مغزی مبتنی بر الکترونسفالوگرافی در هنگام رانندگی واقعی در مردان جوان-2019
In recent years, a large proportion of traffic accidents are caused by driver fatigue. The brain has been conceived as a complex network, whose function can be assessed with EEG. Hence, in this research, fourteen subjects participated in the real driving experiments, and a comprehensive EEG-based expert system was designed for detecting driver fatigue. Collected EEG signals were first decomposed into delta-range, theta-range, alpha-range and beta-range by wavelet packet transform (WPT). Unlike other approaches, a multi-channel network construction method based on Phase Lag Index (PLI) was then proposed in this paper. Finally, the functional connectivity between alert state (at the beginning of the drive) and fatigue state (at the end of the drive) in multiple frequency bands were analyzed. The results indicate that functional connectivity of the brain area was significantly different between alert and fatigue states, especially in alpha-range and beta-range. Particularly, the frontal-to-parietal functional connectivity was weakened. Meanwhile, lower clustering coefficient (C) values and higher characteristic path length (L) values were observed in fatigue state in comparison with alert state. Based on this, two new EEG feature selection approaches, C and L in the corresponding sub-frequency range were applied to feature recognition and classification system. Using a support vector machine (SVM) machine learning algorithm, these features were combined to distinguish between alert and fatigue states, achieving an accuracy of 94.4%, precision of 94.3%, sensitivity of 94.6% and false alarm rate of 5.7%. The results suggest that brain network analysis approaches combined with SVM are helpful to alert drivers while being sleepy or even fatigue.
Keywords: Electroencephalography (EEG) | Driver fatigue | Phase lag index | Graph theory | Functional connectivity | Brain network
مقاله انگلیسی
19 DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal
DOSED: یک رویکرد یادگیری عمیق برای تشخیص ریز وقایع چند خوابی در سیگنال EEG-2019
Background: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so-called sleep stages, which are assigned by experts to every 30s window of signal. For diagnosis, they also rely on shorter prototypical micro-architecture events which exhibit variable durations and shapes, such as spindles, K-complexes or arousals. Annotating such events is traditionally performed by a trained sleep expert, making the process time consuming, tedious and subject to inter-scorer variability. To automate this procedure, various methods have been developed, yet these are event-specific and rely on the extraction of hand-crafted features. New method: We propose a novel deep learning architecture called Dreem One Shot Event Detector (DOSED). DOSED jointly predicts locations, durations and types of events in EEG time series. The proposed approach, applied here on sleep related micro-architecture events, is inspired by object detectors developed for computer vision such as YOLO and SSD. It relies on a convolutional neural network that builds a feature representation from raw EEG signals, as well as two modules performing localization and classification respectively. Results and comparison with other methods: The proposed approach is tested on 4 datasets and 3 types of events (spindles, K-complexes, arousals) and compared to the current state-of-the-art detection algorithms. Conclusions: Results demonstrate the versatility of this new approach and improved performance compared to the current state-of-the-art detection methods.
Keywords: Deep learning | Machine learning | EEG | Event detection | Sleep
مقاله انگلیسی
20 Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram
رویکردهای یادگیری عمیق برای تشخیص خودکار رویدادهای sleep apnea از الکتروکاردیوگرام-2019
Background and Objective: This study demonstrates deep learning approaches with an aim to find the optimal method to automatically detect sleep apnea (SA) events from an electrocardiogram (ECG) signal. Methods: Six deep learning approaches were designed and implemented for automatic detection of SA events including deep neural network (DNN), one-dimensional (1D) convolutional neural networks (CNN), two-dimensional (2D) CNN, recurrent neural networks (RNN), long short-term memory, and gated- recurrent unit (GRU). Designed deep learning models were analyzed and compared in the performances. The ECG signal was pre-processed, normalized, and segmented into 10 s intervals. Subsequently, the sig- nal was converted into a 2D form for analysis in the 2D CNN model. A dataset collected from 86 patients with SA was used. The training set comprised data from 69 of the patients, while the test set contained data from the remaining 17 patients. Results: The accuracy of the best-performing model was 99.0%, and the 1D CNN and GRU models had 99.0% recall rates. Conclusions: The designed deep learning approaches performed better than those developed and tested in previous studies in terms of detecting SA events, and they could distinguish between apnea and hypopnea events using an ECG signal. The deep learning approaches such as 1D CNN and GRU can be helpful tools to automatically detect SA in sleep apnea screening and related studies.
Keywords: Sleep apnea | Deep learning | Convolutional neural network | Recurrent neural network | Long short-term memory | Gated-recurrent unit
مقاله انگلیسی
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