402 research outputs found
Improving time–frequency domain sleep EEG classification via singular spectrum analysis
Background: Manual sleep scoring is deemed to be tedious and time consuming. Even among automatic methods such as Time-Frequency (T-F) representations, there is still room for more improvement.
New method: To optimise the efficiency of T-F domain analysis of sleep electroencephalography (EEG) a novel approach for automatically identifying the brain waves, sleep spindles, and K-complexes from the sleep EEG signals is proposed. The proposed method is based on singular spectrum analysis (SSA). The single-channel EEG signal (C3-A2) is initially decomposed and then the desired components are automatically separated. In addition, the noise is removed to enhance the discrimination ability of features. The obtained T-F features after preprocessing stage are classified using a multi-class support vector machines (SVM) and used for the identification of four sleep stages over three sleep types. Furthermore, to emphasize on the usefulness of the proposed method the automatically-determined spindles are parameterised to discriminate three sleep types.
Result: The four sleep stages are classified through SVM twice: with and without preprocessing stage. The mean accuracy, sensitivity, and specificity for before the preprocessing stage are: 71.5 ± 0.11%, 56.1 ± 0.09% and 86.8 ± 0.04% respectively. However, these values increase significantly to 83.6 ± 0.07%, 70.6 ± 0.14% and 90.8 ± 0.03% after applying SSA.
Comparison with existing method: The new T-F representation has been compared with the existing benchmarks. Our results prove that, the proposed method well outperforms the previous methods in terms of identification and representation of sleep stages.
Conclusion: Experimental results confirm the performance improvement in terms of classification rate and also representative T-F domain
Penetrating Neck Trauma: Review of 192 Cases
Background: The neck region contains a high density of vital organ structures within a relatively small and unprotected anatomic region, making it one of the most vulnerable areas of the body for all types of injuries.
Objectives: In this article, we studied penetrating neck trauma cases in Alzahra Hospital over a 10-year period.
Patients and Methods: In this retrospective, descriptive, analytical study, penetrating neck trauma cases admitted to Alzahra Hospital between April 2000 and April 2010 were analyzed for epidemiology, mechanism of trauma, zone of trauma, therapeutic method, injuries to other organs, complications, and mortality.
Results: Among 192 penetrating neck injuries, the mean age at the time of injury was 25.08 ± 15.02 years. Of these cases, 96.4% percent occurred in men. The most common mechanisms of trauma was stab wounds (85.93%). In 56.3% of penetrating neck injuries, zone 2 was involved. Neck exploration was positive in 84.4% of cases, and 52.1% of patients underwent surgery. Vascular exploration was the most common cause of surgery (67.2% of patients). The most common surgical intervention was vein ligation (50.8% of cases). In 11.98% of cases, another organ injury occurred simultaneously, and chest injury was the most common coexisting problem (65.2%). Complications were reported in 9.3% of patients, and the need for intubation was the most common complication (5.2% of patients). Mortality rate was 1.5%.
Conclusions: According to the findings of this study, the most common cause of penetrating neck injuries was stab wounds, and the majority of patients were young men, therefore, preventive measures should be implemented. Because of fatal complications associated with neck injuries, we recommend early neck exploration in unstable cases or when injuries are deeper than the platysma
Vision-based techniques for gait recognition
Global security concerns have raised a proliferation of video surveillance
devices. Intelligent surveillance systems seek to discover possible threats
automatically and raise alerts. Being able to identify the surveyed object can
help determine its threat level. The current generation of devices provide
digital video data to be analysed for time varying features to assist in the
identification process. Commonly, people queue up to access a facility and
approach a video camera in full frontal view. In this environment, a variety of
biometrics are available - for example, gait which includes temporal features
like stride period. Gait can be measured unobtrusively at a distance. The video
data will also include face features, which are short-range biometrics. In this
way, one can combine biometrics naturally using one set of data. In this paper
we survey current techniques of gait recognition and modelling with the
environment in which the research was conducted. We also discuss in detail the
issues arising from deriving gait data, such as perspective and occlusion
effects, together with the associated computer vision challenges of reliable
tracking of human movement. Then, after highlighting these issues and
challenges related to gait processing, we proceed to discuss the frameworks
combining gait with other biometrics. We then provide motivations for a novel
paradigm in biometrics-based human recognition, i.e. the use of the
fronto-normal view of gait as a far-range biometrics combined with biometrics
operating at a near distance
Separation and localisation of P300 sources and their subcomponents using constrained blind source separation
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A Bayesian real-time electric vehicle charging strategy for mitigating renewable energy fluctuations
A novel pricing and scheduling mechanism is proposed here for Plug-in electric vehicles (PEVs) charging/discharging to track and synchronize with a renewable power generation pattern. Moreover, the proposed mechanism can be used in the demand-side management and ancillary service applications, respectively for the peak shaving and frequency regulation responding. We design a fully distributed stochastic optimization mechanism using Bayesian pure strategic repeated game by which the PEVs optimally schedule their demands. We also use a mixed Bayesian-diffusion Kalman filtering strategy for the customers to collaboratively estimate and track the stochastic price and regulation signals for the upcoming scheduling window. In the proposed paper all the characteristics of the PEVs, as well as the uncertainty about their deriving patterns are considered. As our framework converges to an equilibrium even with incomplete information, is agent-based, and the agents share the information only with their optional neighbors, it is scale-free, robust, and secure
Seroepidemiology of rubella, measles, HBV, HCV and B19 virus within women in child bearing ages (Saravan City of Sistan and Bloochastan Province)
Present survey basically focused on women between 15-45 years of age resident in a town of Sistan and Baluchistan province named as Saravan city located in border of Pakistan-Iran in order to find out the seropositivity against the viruses in child bearing ages in the above stated under study community. This descriptive cross-sectional study was carried-out from 2001 up to 2002. Saravan town was divided into 4 geographical areas and each area was further sub-divided into 10 blocks and in each block 10 families were chosen randomly. In the next step by referring to each family from the chosen married women with specified age i.e., 15-45 years, 5 mL blood was collected. Serum was then separated and stored at -20°C before the assay. ELISA kit was employed to detect anti B19, anti rubella, anti measles, anti HBV and anti HCV antibody. Furthermore during samples collection a questionnaire filled for each woman under study. This study showed that 89.6% of women understudy were seropositive against measles, rubella (96.2%), B19 (59.2%), HCV (0.8%) and HBV (19.8%), respectively. According to the results of no serious problem with rubella in this area; But, about measles, the present immunity against measles in this area is insufficient. It seems that incidence of B19 infection in this region is same as other places in Iran. The rate of seropositivity against HBV and HCV indicated of these viruses circulating in the population in this area. © 2007 Academic Journals
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A robust scalable demand-side management based on diffusion-ADMM strategy for smart grid
Demand-side management (DSM) involves a group of programs, initiatives, and technologies designed to encourage consumers to modify their level and pattern of electricity usage. This is performed following methods such as financial incentives and behavioral change through education. While the objective of the DSM is to achieve a balance between energy production and demand, effective and efficient implementation of the program rests within effective use of emerging Internet of things (IoT) concept for online interactions. Here, a novel DSM framework based on diffusion and alternating direction method of multipliers (ADMM) strategies, repeated under a model predictive control (MPC) protocol, is proposed. On the demand side, the customers autonomously and by cooperation with their immediate neighbors estimate the baseline price in real time. Based on the estimated price signal, the customers schedule their energy consumption using the ADMM cost-sharing strategy to minimize their incommodity level. On the supply side, the utility company determines the price parameters based on the customers real-time behavior to make a profit and prevent the infrastructure overload. The proposed mechanism is capable of tracking drifts in the optimal solution resulting from the changes in supply/demand sides. Moreover, it considers all classes of appliances by formulating the DSM problem as a mixed-integer programming (MIP) problem. Numerical examples are provided to show the effectiveness of the proposed framework
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