Search results for: Armin Rahimi

Authors: Rahimi-Movaghar Vafa, Mansouri Pejman, Chardoli Mojtaba, Rezvani Samina

Abstract:

Introduction: Blunt abdominal trauma is one of the leading causes of morbidity and mortality in all age groups, but diagnosis of serious intra-abdominal pathology is difficult and most of the damages are obscure in the initial investigation. There is still controversy about which patients should undergo abdomen/pelvis CT, which patients needs more observation and which patients can be discharged safely The aim of this study was to determine that is it safe to discharge patients with blunt abdominal trauma with normal initial findings. Methods: This non-randomized cross-sectional study was conducted from September 2013 to September 2014 at two levels I trauma centers, Sina hospital and Rasoul-e-Akram hospital (Tehran, Iran). Our inclusion criteria were all patients were admitted for suspicious BAT and our exclusion criteria were patients that have serious head and neck, chest, spine and limb injuries which need surgical intervention, those who have unstable vital signs, pregnant women with a gestational age over 3 months and homeless or without exact home address. 390 patients with blunt trauma abdomen examined and the necessary data, including demographic data, the abdominal examination, FAST result, patients’ lab test results (hematocrit, base deficit, urine analysis) on admission and at 6 and 12 hours after admission were recorded. Patients with normal physical examination, laboratory tests and FAST were discharged from the ED during 12 hours with the explanation of the alarm signs and were followed up after 24 hours and 1 week by a telephone call. Patients with abnormal findings in physical examination, laboratory tests, and FAST underwent abdomino-pelvic CT scan. Results: The study included 390 patients with blunt abdominal trauma between 12 and 80 years of age (mean age, 37.0 ± 13.7 years) and the mean duration of hospitalization in patients was 7.4 ± 4.1 hours. 88.6% of the patients were discharged from hospital before 12 hours. Odds ratio (OR) for having any symptoms for discharge after 6 hours was 0.160 and after 12 hours was 0.117 hours, which is statistically significant. Among the variables age, systolic and diastolic blood pressure, heart rate, respiratory rate, hematocrit and base deficit at admission, 6 hours and 12 hours after admission showed no significant statistical relationship with discharge time. From our 390 patients, 190 patients have normal initial physical examination, lab data and FAST findings that didn’t show any signs or symptoms in their next assessment and in their follow up by the phone call. Conclusion: It is recommended that patients with no symptoms at admission (completely normal physical examination, ultrasound, normal hematocrit and normal base deficit and lack of microscopic hematuria) and good family and social status can be safely discharged from the emergency department.

Keywords: blunt abdominal trauma, patient discharge, emergency department, FAST

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

Abstract:

Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: David Dolbow, Daniel Credeur, Mujtaba Rahimi, Dobrivoje Stokic, Jennifer Lemacks, Andrew Courtner

Abstract:

Introduction: Obesity is at epidemic proportions in the spinal cord injury (SCI) population (66-75%), as individuals who suffer from paralysis undergo a dramatic decrease in muscle mass and a dramatic increase in adipose deposition. Obesity is a major public health concern which includes a doubling of the risk of heart disease, stroke and type II diabetes mellitus. It has been demonstrated that physical activity, and especially HIIT, can promote a healthy body composition and decrease the risk cardiometabolic disease in the able-bodied population. However, SCI typically limits voluntary exercise to the arms, but a high prevalence of shoulder pain in persons with chronic SCI (60-90%) can cause increased arm exercise to be problematic. Functional electrical stimulation (FES) cycling has proven to be a safe and effective way to exercise paralyzed leg muscles in clinical and home settings, saving the often overworked arms. Yet, HIIT-FES cycling had not been investigated prior to the current study. The purpose of this study was to investigate the body composition changes with combined HIIT-FES cycling and nutritional counseling on individuals with SCI. Design: A matched (level of injury, time since injury, body mass index) and controlled trail. Setting: University exercise performance laboratory. Subjects: Ten individuals with chronic SCI (C5-T9) ASIA impairment classification (A & B) were divided into the treatment group (n=5) for 30 minutes of HIIT-FES cycling 3 times per week for 8 weeks and nutritional counseling over the phone for 30 minutes once per week for 8 weeks and the control group (n=5) who received nutritional counseling only. Results: There was a statistically significant difference between the HIIT-FES group and the control group in mean body fat percentage change (-1.14 to +0.24) respectively, p = .030). There was also a statistically significant difference between the HIIT-FES and control groups in mean change in legs lean mass (+0.78 kg to -1.5 kg) respectively, p = 0.004. There was a nominal decrease in weight, BMI, total fat mass and a nominal increase in total lean mass for the HIIT-FES group over the control group. However, these changes were not found to be statistically significant. Additionally, there was a nominal decrease in the mean blood glucose levels for both groups 101.8 to 97.8 mg/dl for the HIIT-FES group and 94.6 to 93 mg/dl for the Nutrition only group, however, neither were found to be statistically significant. Conclusion: HIIT-FES cycling combined with nutritional counseling can provide healthful body composition changes including decreased body fat percentage in just 8 weeks. Future study recommendations include a greater number of participants, a primer electrical stimulation exercise program to better ready participants for HIIT-FES cycling and a greater volume of training above 30 minutes, 3 times per week for 8 weeks.

Keywords: body composition, functional electrical stimulation cycling, high-intensity interval training, spinal cord injury

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