Brain-computer interface systems aim to facilitate human-computer interactions in a great deal by direct translation of brain signals for computers. Recently, using many electrodes has caused better ...performance in these systems. However, increasing the number of recorded electrodes leads to additional time, hardware, and computational costs besides undesired complications of the recording process. Channel selection has been utilized to decrease data dimension and eliminate irrelevant channels while reducing the noise effects. Furthermore, the technique lowers the time and computational costs in real-time applications. We present a channel selection method, which combines a sequential search method with a genetic algorithm called Deep GA Fitness Formation (DGAFF). The proposed method accelerates the convergence of the genetic algorithm and increases the system's performance. The system evaluation is based on a lightweight deep neural network that automates the whole model training process. The proposed method outperforms other channel selection methods in classifying motor imagery on the utilized dataset.
Coronavirus has caused hundreds of thousands of deaths. Fatalities could decrease if every patient could get suitable treatment by the healthcare system. Machine learning, especially computer vision ...methods based on deep learning, can help healthcare professionals diagnose and treat COVID-19 infected cases more efficiently. Hence, infected patients can get better service from the healthcare system and decrease the number of deaths caused by the coronavirus. This research proposes a method for segmenting infected lung regions in a CT image. For this purpose, a convolutional neural network with an attention mechanism is used to detect infected areas with complex patterns. Attention blocks improve the segmentation accuracy by focusing on informative parts of the image. Furthermore, a generative adversarial network generates synthetic images for data augmentation and expansion of small available datasets. Experimental results show the superiority of the proposed method compared to some existing procedures.
Brain signals could be used to control devices to assist individuals with disabilities. Signals such as electroencephalograms are complicated and hard to interpret. A set of signals are collected and ...should be classified to identify the intention of the subject. Different approaches have tried to reduce the number of channels before sending them to a classifier. We are proposing a deep learning-based method for selecting an informative subset of channels that produce high classification accuracy. The proposed network could be trained for an individual subject for the selection of an appropriate set of channels. Reduction of the number of channels could reduce the complexity of brain-computer-interface devices. Our method could find a subset of channels. The accuracy of our approach is comparable with a model trained on all channels. Hence, our model's temporal and power costs are low, while its accuracy is kept high.
The aim of the study was to determine the prevalence of metabolic syndrome (MeS) in professional bus drivers, and its association with overtime working hours among those drivers in Urmia, Iran. In ...this cross sectional study the studies population was 626 professional bus drivers, aged 20-69 yr. The MeS (according to theNational Cholesterol EducationProgram Adult Treatment Panel III), Waist circumference, Systolic blood pressure, Diastolic blood pressure, Fasting plasma glucose, Triglyceride, HDL-cholesterol, age, and working time per week. The overall prevalence of the metabolic syndrome was 32.4%. The prevalence of the MeS was higher than the general Iranian population. There was a statistically significant positive relationship between over time driving and MeS (P: 0.028). This represents an odds ratio of 1.46 (95%CI: 1.04 – 2.05). The metabolic syndrome is becoming a noteworthy health problem in bus drivers; therefore, early detection and appropriate intervention need to be established.
The aim of the study was to determine the prevalence of metabolic syndrome (MeS) in professional bus drivers, and its association with overtime working hours among those drivers in Urmia, Iran. In ...this cross sectional study the studies population was 626 professional bus drivers, aged 20-69 yr. The MeS (according to the National Cholesterol Education Program Adult Treatment Panel III), Waist circumference, Systolic blood pressure, Diastolic blood pressure, Fasting plasma glucose, Triglyceride, HDL-cholesterol, age, and working time per week. The overall prevalence of the metabolic syndrome was 32.4%. The prevalence of the MeS was higher than the general Iranian population. There was a statistically significant positive relationship between over time driving and MeS (P: 0.028). This represents an odds ratio of 1.46 (95%CI: 1.04 – 2.05). The metabolic syndrome is becoming a noteworthy health problem in bus drivers; therefore, early detection and appropriate intervention need to be established.
The new coronavirus infection has shocked the world since early 2020 with its aggressive outbreak. Rapid detection of the disease saves lives, and relying on medical imaging (Computed Tomography and ...X-ray) to detect infected lungs has shown to be effective. Deep learning and convolutional neural networks have been used for image analysis in this context. However, accurate identification of infected regions has proven challenging for two main reasons. Firstly, the characteristics of infected areas differ in different images. Secondly, insufficient training data makes it challenging to train various machine learning algorithms, including deep-learning models. This paper proposes an approach to segment lung regions infected by COVID-19 to help cardiologists diagnose the disease more accurately, faster, and more manageable. We propose a bifurcated 2-D model for two types of segmentation. This model uses a shared encoder and a bifurcated connection to two separate decoders. One decoder is for segmentation of the healthy region of the lungs, while the other is for the segmentation of the infected regions. Experiments on publically available images show that the bifurcated structure segments infected regions of the lungs better than state of the art.
