دانلود و نمایش مقالات مرتبط با Sleep::صفحه 3
بلافاصله پس از پرداخت دانلود کنید
نتیجه جستجو - Sleep

تعداد مقالات یافته شده: 48
ردیف عنوان نوع
21 Slow wave detection in sleeping mice: Comparison of traditional and machine learning methods
تشخیص موج آهسته در موش های خواب: مقایسه روش های یادگیری سنتی و ماشین-2019
Background: During slow-wave sleep the electroencephalographic (EEG) and local field potential (LFP) recordings reveal the presence of large amplitude slow waves. Systematic extraction of individual slow waves is not trivial. New method: In this study, we used the neural network pattern recognition to detect individual slow waves in LFP recorded from mice as well as other commonly used methods that are based on fast frequencies modulation, amplitude, or duration. Results: The number and quality of events detected as slow waves depended on the chosen method of detection, level of thresholds, or on combination of methods. Each individual method yields some false-positive and falsenegative detections. Typically, the fast frequency-method has a higher false discovery rate, but almost no missing waves; amplitude-based method has relatively high false-positive and false-negative rates; duration-based method has low false-negative rates; neural network pattern recognition approach has the lowest false-positive rate among individual methods, often rejecting waves that were falsely detected by other approaches. Combining all 4 detection methods practically eliminated false-positive errors, but a large number of slow waves remained undetected. Conclusions: The use of a particular method of slow wave detection needs to be adjusted to the objectives of a given study: to detect all slow waves, but also numerous false positives can be achieved using the fast frequency approach. Neural network pattern recognition method alone can detect slow waves with the lowest false-positive rate, that can be further minimized with the use of combination of other methods.
Keywords: Slow-wave sleep | Slow waves | Automatic methods | Artificial neural network
مقاله انگلیسی
22 Cross-subject network investigation of the EEG microstructure: A sleep spindles study
بررسی شبکه ای موضعی از ساختار EEG: یک مطالعه sleep spindles-2019
Background: The microstructural EEG elements and their functional networks relate to many neurophysiological functions of the brain and can reveal abnormalities. Despite the blooming variety of methods for estimating connectivity in the EEG of a single subject, a common pitfall is seen in relevant studies; grand averaging is used for estimating the characteristic connectivity patterns of a group of subjects. This averaging may distort results and fail to account for the internal variability of connectivity results across the subjects of a group. New Method: In this study, we propose a novel methodology for the cross-subject network investigation of EEG graphoelements. We used dimensionality reduction techniques in order to reveal internal connectivity properties and to examine how consistent these are across a number of subjects. In addition, graph theoretical measures were utilized to prioritize regions according to their network attributes. Results: As proof of concept, we applied this method on fast sleep spindles across 10 healthy subjects. Neurophysiological findings revealed subnetworks of the spindle events across subjects, highlighting a predominance for occipito-parietal areas and their connectivity with frontal regions. Comparison with existing methods: This is a new approach for the examination of within-group connectivities in EEG research. The results accounted for more than 85% of the overall data variance and the detected subnetworks were found to be meaningful down-projections of the grand average of the group, suggesting sufficient performance for the proposed methodology. Conclusion: We conclude that the proposed methodology can serve as an observatory tool for the EEG connectivity patterns across subjects, providing a supplementary analysis of the existing topography techniques.
Keywords:EEG networks | PCA | EEG microstructure | Sleep spindle networks | Graph theory | Pattern recognition
مقاله انگلیسی
23 Does evidence support “banking/extending sleep” by shift workers to mitigate fatigue, and/or to improve health, safety, or performance? A systematic review
آیا شواهد با تغییر کارگران برای کاهش خستگی ، و یا بهبود سلامت ، ایمنی یا عملکرد از "بانکی / تمدید خواب" پشتیبانی می کنند؟ یک مرور منظم-2019
Background: Sleep deprivation is common in shiftwork occupations, including safety-sensitive occupations. While extending sleep prior to scheduled shifts (i.e., “banking sleep”) may be an intuitive strategy for fatigue mitigation, the evidence behind this strategy is unclear. Methods:Weperformed a systematic review of literature retrieved in searches of four databases.We examined agreement between two independent screeners, abstracted key findings, reviewed and synthesized findings, and evaluated the quality of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. The Cochrane Collaborations risk of bias tool was used to evaluate bias of individual studies. We reported findings as prescribed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Results: Of the 3007 records screened, five met inclusion criteria. The inter-rater agreement for inclusion/ exclusionwas high (κ=0.87). One study addressed patient safety outcomes. Four studies assessed the impact of banking sleep on performance, five assessed measures of acute fatigue, and three evaluated banking sleep on indicators of health. All five studies presented a very serious risk of bias and the quality of evidence was very low. Given these caveats, the findings, in aggregate, support banking sleep as a strategy to improve indicators of performance and acute fatigue. Conclusions: This systematic review identifies gaps in research of shift workers on the efficacy of banking sleep as a fatigue risk management strategy. The available evidence supports banking sleep prior to shiftwork as a strategy for improved patient safety, performance, and reducing acute fatigue.
Keywords: Sleep | Extended sleep | Shift workers | Safety
مقاله انگلیسی
24 Semi-Supervised Learning Based Big Data-Driven Anomaly Detection in Mobile Wireless Networks
تشخیص ناهنجاری های رانده شده با داده های نیمه نظارت بر اساس داده ها در شبکه های بی سیم سیار-2018
With rising capacity demand in mobile networks, the infrastructure is also becoming increasingly denser and complex. This results in collection of larger amount of raw data (big data) that is generated at different levels of network architecture and is typically underutilized. To unleash its full value, innovative machine learning algorithms need to be utilized in order to extract valuable insights which can be used for improving the overall network’s performance. Additionally, a major challenge for network operators is to cope up with increasing number of complete (or partial) cell outages and to simultaneously reduce operational expenditure. This paper contributes towards the aforementioned problems by exploiting big data generated from the core network of 4G LTE-A to detect network’s anomalous behavior. We present a semi-supervised statistical-based anomaly detection technique to identify in time: first, unusually low user activity region depicting sleeping cell, which is a special case of cell outage; and second, unusually high user traffic area corresponding to a situation where special action such as additional resource allocation, fault avoidance solution etc. may be needed. Achieved results demonstrate that the proposed method can be used for timely and reliable anomaly detection in current and future cellular networks.
Keywords: 5G; 4G LTE-A; anomaly detec tion; call detail record; machine learning; big data analytics; network behavior analysis; sleeping cell
مقاله انگلیسی
25 PRMT پیش بینی فاکتورهای خطر ابتلا به چاقی در افراد میان سال با استفاده از تکنیک داده کاوی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 16
چاقی یک وضعیت آناتومی است که با رشد شدید چربی بدن مشخص می شود. میزان چاقی به تدریج افزایش می یابد، با توجه به مقالات پیشین، چاقی بیماری جدی سلامت در جهان است. در این مطالعه 259 داده از مناطق شهری و روستایی با توجه به میزان مختلف خطر ناشی از فعالیت های روزمره جمع آوری شد. هدف از مطالعه، شبیه سازی عامل خطر با استفاده از ابزار آماری (SPSS) است که به پیش بینی عامل اصلی خطر چاقی با تست سطح کلاس و مطالعه مقطعی دیگر ویژگی ها کمک می کند. با آنالیز (P-value (p <0.05، دریافتیم سن (0.002)، قد (0.002)، وزن( 0.000)، شیوه زندگی سالم (0.000)، وضعیت زناشویی (0.001)، BMI ( 0.000) ، اقتصادی (0.028)، خواب در روز (0.011) دارای ارتباط معناداری با کلاس چاقی دارد. در این مطالعه یک روش ریسک داده کاوی (PRMT) برای پیش بینی مدلی به منظور تحلیل عامل خطر ابتلا به چاقی با استفاده از طبقه بندی های مختلف داده کاوی ، با استفاده از WEKA برای برآورد دقت و خطا پیشنهاد شد. نتیجه این فرایند Naïve Bayes بهترین طبقه بندی برای مطالعه 10 برابر اعتبارسنجی است. مدل پیشنهادی برای پیش بینی عامل انسانی موثر در کنترل و کاهش بیماری قلبی عروقی است.
کليدواژه ها: چاقي | خطر سرماخوردگي | بیماری قلبی عروقی | طبقه بندی
مقاله ترجمه شده
26 Agent-Based Simulation of Smart Beds With Internet-of-Things for Exploring Big Data Analytics
شبیه سازی مبتنی بر عامل تختهای هوشمند با استفاده از اینترنت اشیا برای تحلیل داده های بزرگ-2018
Internet-of-Things (IoT) can allow healthcare professionals to remotely monitor patients by analyzing the sensors outputs with big data analytics. Sleeping conditions are one of the most influential factors on health. However, the literature lacks of the appropriate simulation tools to widely support the research on the recognition of sleeping postures. This paper proposes an agent-based simulation framework to simulate sleeper movements on a simulated smart bed with load sensors. This framework allows one to define sleeping posture recognition algorithms and compare their outcomes with the poses adopted by the sleeper. This novel presented ABS-BedIoT simulator allows users to graphically explore the results with starplots, evolution charts, and final visual representations of the states of the bed sensors. This simulator can also generate logs text files with big data for applying offline big data techniques on them. The source code of ABS-BedIoT and some examples of logs are freely available from a public research repository. The current approach is illustrated with an algorithm that properly recognized the simulated sleeping postures with an average accuracy of 98%. This accuracy is higher than the one reported by an existing alternative work in this area.
INDEX TERMS : Agent-based-simulation, big data, Internet-of-Things, multi-agent systems, smart bed
مقاله انگلیسی
27 Exploiting Industrial Big Data Strategy for Load Balancing in Industrial Wireless Mobile Networks
استراتژی بهره برداری از داده های بزرگ صنعتی برای تعادل بار در شبکه های موبایل بی سیم صنعتی-2018
In the era of big data, traditional industrial mobile wireless networks cannot effectively handle the new requirements of mobile wireless big data networks arising from the spatio-temporal changes of a nodes traffic load. From the perspective of load balancing and energy efficiency, industrial big data (IBD) brings new transmission challenges to industrial wireless mobile networks (IWMNs). Previous research works have not considered dynamic changes related to the traffic and mobility of IWMNs. In this paper, using an IBD technique, we propose a novel second-deployment and sleep-scheduling strategy (SDSS) for balancing load and increasing energy efficiency, while taking the dynamic nature of the network into consideration. SDSS can be divided into two stages. In the first stage, changes in the traffic of every network grid and its maximum traffic load at different times are calculated using big data analysis techniques. In the second stage, a second-deployment method for the cluster head nodes (CHNs), based on each grids maximum traffic load, is adopted. To save energy, based on their position and traffic states, a sleep-wake scheduling is presented for the CHNs. Simulations results verify the effectiveness of this methodology to save energy and obtain a traffic balance, which is more efficient than obtained through traditional methods.
INDEX TERMS: Load balancing, energy efficiency, sleep scheduling, second-deployment, industrial wireless networks, big data networks
مقاله انگلیسی
28 Effect of sleep deprivation after a night shift duty on simulated crisis management by residents in anaesthesia. A randomised crossover study
تأثیر محرومیت از خواب پس از یک بار تغییر شغل در مدیریت بحران شبیه سازی توسط ساکنین در بیهوشی. یک مطالعه متقاطع تصادفی-2018
Background: Sleep deprivation has been associated with an increased incidence of medical errors and can jeopardise patients’ safety during medical crisis management. The aim of the study was to assess the effect of sleep deprivation on the management of simulated anaesthesia crisis by residents in anaesthesiology. Methods: A randomised, comparative, monocentric crossover study involving 48 residents in anaesthesia was performed on a high fidelity patient simulator. Each resident was evaluated in a sleep-deprived state (deprived group, after a night shift duty) and control state (control group, after a night of sleep). Performance was assessed through points obtained during crisis scenario 1 (oesophageal intubation followed by anaphylactic shock) and scenario 2 (anaesthesia-related bronchospasm followed by ventricular tachycardia). Sleep periods were recorded by actigraphy. Two independent observers assessed the performances. The primary endpoint of the study was the score obtained for each scenario. Results: Resident’s crisis management performance is associated with sleep deprivation (scenario 1: control = 39 [33–42] points vs. deprived = 26 [19–40] points, P = 0.02; scenario 2: control = 21 [17–24] vs. deprived = 14 [12–19], P = 0.01). The main errors observed were: error in drug administration and dose, delay in identification of hypotension, and missing communication with the surgical team about situation. Conclusions: The present study showed that sleep deprivation is associated with impairment of performance to manage crisis situations by residents in anaesthesia.
Keywords: Simulation ، Sleep deprivation ، Patient safety ، Anaesthesia
مقاله انگلیسی
29 A pilot study to explore the effects of substances on cognition, mood, performance, and experience of daily activities
یک مطالعه آزمایشی جهت بررسی تاثیرات مواد روی شناخت، حالت، عملکرد و تجربه فعالیتهای روزانه-2018
Purpose This pilot study was designed to deliberately examine the enhancement effects and experiences of substances used among professionals and students in professional programs. Methods A mixed methods design was implemented, involving ecological momentary assessment (EMA) and interviews. The analysis presents interpretations about the perceived impact of substance use on the performance and experience of everyday activities. Results Caffeine, alcohol, antidepressants, pain suppressant, and cannabis were used by the most participant. Participants reported effects of substances that directly or indirectly enhanced performance (e.g., sleep, socialisation), mood (e.g., manage stress, relax), cognition (e.g., energy and clarity of thought), and the general experience of activities (e.g., enjoyment). Less common effects included impaired work, school, or leisure performance, injury, sleep disruption, and pain or discomfort. Reactivity was an unexpected effect, with almost half of the interviewees reporting changes in their thoughts about their substance use, and 30% of interviewees making active changes. Conclusion This study was novel in population and data collection. Complex perspectives about substance use were offered by recruiting professionals and students outside at-risk populations or addiction-related services. By examining effects of substances, this research offers nuanced understandings of self-reported effects of psychoactive substances on performance, mood, cognition, and quality of experience.
keywords: Substance use |Professionals |Professional students |Performance |Experience |Ecological momentary assessment |Substance effects
مقاله انگلیسی
30 Mining Social Media Data for Understanding Students Learning Experiences using Memetic algorithm
کاوش داده های رسانه های اجتماعی برای درک تجربیات یادگیری دانش آموزان با استفاده از الگوریتم Memetic-2018
Now a day’s most of students communicate with each other using various social media networks such as Twitter, Facebook, YouTube and what’s app. Students shares their opinions, concerns and emotions about the learning process. From these social sites there are large size of unstructured data are generated which consists students important data. To manage this unstructured data are too difficult task, so we use various techniques to solve this problem. In this paper we collect all Engineering students communication from twitter to analyse various problems like heavy study load, negative emotions, lack of social engagement and sleepy problems. Students’ comments from twitter are classified into various above problem using Naïve Bayes algorithm. Also we used various algorithms for processing data like stemming, TF-IDF algorithm and cosine similarity. This paper shows that how students share their opinions through twitter and which comments are in which category. Using Memetic algorithm we got the more accurate results.
Keywords: Education;problems; computers and education; social networkin; web text analysis
مقاله انگلیسی
rss مقالات ترجمه شده rss مقالات انگلیسی rss کتاب های انگلیسی rss مقالات آموزشی
logo-samandehi