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Showing 13–24 of 180 results
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Evaluation of 2D and 3D Deep Learning Approaches for Automatic Segmentation of the Retinal External Limiting Membrane in Spectral Domain Optical Coherence Tomography Images
Abstract: In this work, we compared the performance of 2D and 3D versions of three state-of-the-art deep neural networks on segmenting the retinal external limiting membrane (ELM) using a publicly available image dataset of spectral-domain optical coherence tomography (OCT) scans. Based on our results, 3D networks generally outperformed the 2D networks in Dice coefficient, mean surface distance and false positive rate but lagged behind in Hausdorff distance. 3D networks also produce smoother surfaces based on mean surface curvedness.
Keywords: 2D vs. 3D Image Segmentation, Machine Learning, Annotation, External Limiting Membrane, OCT.
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A sparse-NTF-based feature space construction method for automatic sleep stage identification
Abstract: Healthy sleep is essential for the normal functioning of the human body and for the maintenance of mental vitality. One’s overnight sleep is usually evaluated by several sleep stages with sleep cycles. Automatic sleep stage identification methods are effective tools for sleep staging where feature extraction is an important procedure affecting the identification performance. In this study, an automatic sleep staging method based on multi-channel EEG signals and its optimized tensor feature space is developed. Several characteristic features are calculated from the two EEG recording channels and constructed as the original tensor feature space. A non-negative tensor factorization method based on sparse improvement is developed to optimize the tensor feature space for sleep staging. A classification model is constructed based on BP neural network and the parameters are estimated by PSO algorithm. Totally 10 overnight sleep recordings were tested. The averaged classification accuracy is about 84%. The developed method can be an assistant computerized tool for sleep staging.
Keywords: Sleep staging; EEG; Non-negative tensor factorization; BP neural network; PSO
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Deploying Electronic Health Records In Sports
Abstract: EHR has the advantage of providing and updating information instantaneously at any time and place, giving it a potential advantage for use in sports events and not just in hospital settings. With this in mind, we address the question: do frontline sports health providers, coaches, and athletes prefer traditional paper charts, or do they prefer a paperless version of the incident report, such as EHR? The research was conducted over three days at an organized outdoor summer sporting event, where medical providers used the EHR (Diya Sports Suite) platform to document the treatment of athletes plus spectators. Data was collected via post-event survey questionnaires from coaches, providers, and scribes to solicit their feedback on the EHR. For the medical providers, 86% of them feel that using EHR makes the treatment experience less stressful than traditional paperwork. Overall, 86% of the providers, 62% of the coaches, and 100% of the scribes prefer a paperless medical record system over the traditional paper recording system. However, this study was limited by the low number (12 documentation) of patient incidents seen by the medical team and the low number of people surveyed (19 participants). A future direction for using EHR in this sporting event would be to collect enough incidents to run analytics. Studying this data will help event organizers to identify factors that may hinder the success of the tournament and take precautions to ensure the safety of athletes, coaches, and the audience.
Keywords: Electronic health record, sporting venue
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Brain-Computer Interface for Image Retrieval from EEG
Abstract: This research is motivated to gift the ability of expression to the speech deprived by building a module that extracts thoughts from the human brain and presents it to the external world as a digital image. This novel approach reconstructs the same images of different colours from the EEG brain waves. Standalone datasets are used to train the model, obtained using the Enobio8 headset. EEG signals are acquired by providing six different shapes of three unique colors as the visual stimuli. After processing these signals, EEG features are extracted using a convolutional Neural Network (CNN). The extracted features alongside noise are passed as an input to conditional Deep Convolutional Generative Adversarial Networks (cDCGANs) and conditional Generative Adversarial Networks (cGANs), which reconstruct the visual stimuli corresponding to the EEG data. The results obtained by the same are recorded and examined. The paper explicitly compares the working and performance of the cGAN and cDCGAN models.
Keywords: EEG, Image reconstruction, Deep Learning, Brain Media, Generative adversarial networks (GANs).
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Predicting Oxygen Utilization & Nurse Staffing Needs For SARS-CoV-2
Abstract: Supplemental oxygen is an essential part of in-hospital care for most patients hospitalized with SARS-CoV-2 pneumonia. This study seeks to identify hospitalized patients who will require advanced oxygen support (high flow oxygen, CPAP, BiPAP, or mechanical ventilation) within 96 hours of admission using a machine learning model. This information can be useful for hospitals to plan for nurse staffing, as patients will consume more resources and will need more staff assistance.
Data from 302 SARS-CoV-2 patients was used to create a classifier to predict whether or not patients would require advanced oxygen support within 96 hours of admission. Of the 302 cases, 211 were randomly selected to train the model, and 91 were randomly selected for testing. Through a labeled dataset, we performed supervised learning by using a random forest ensemble model which included demographic, clinical comorbidities, vitals, and laboratory values. We used 5-fold cross-validation to evaluate our trained model and employed a majority vote decision across the five trained models in order to produce the final prediction for a given patient. Through the models, we yielded results through sensitivity, specificity, positive predictive value, negative predictive value, and F1 score with the 91 cases of training data. An additional 24 cases were used to test the validity of the ensemble consensus model. Approximately 40% of all patients progressed to require advanced oxygen support 96 hours after the initial presentation. Although the insight gained from the model may not definitively predict the course of an individual patient, this model may help hospital administrators plan for staffing needs with a 48-hour lead time. Patients on high oxygen support require high acuity beds, which have increased nurse-to-patient ratios. Additional samples may increase its statistical significance. Nevertheless, this model demonstrates the potential and viability of using data science to help manage hospital resources.
Keywords: COVID-19, nurse staffing, resource management, machine learning, oxygen utilization
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A Research on Force Estimation from EMG with a CNN-based Deep Model
Abstract: This paper develops data-driven deep model to estimate muscle contraction force from surface electromyography (EMG) signals. The proposed estimation model is based on deep convolutional neural networks (CNNs). Information of EMG signals from both time and frequency domains has been utilized as input data to two CNN branches. Raw EMG signals are directly input into one branch in order to extract time domain characteristics, while frequency information is fed into the other CNN branch. These two branches are summarized at a concatenation layer, which is followed by three full-connected layers to estimate force levels. End point force at hand, considering the dumbbell curl exercises, has been measured using a testing apparatus, which was designed with a single point load cell. The force data is used as training data of the deep CNN EMG-force model. For validation, different structures of the CNN model are examined with an estimation index of coefficient of determination using EMG and force data of nine subjects
Keywords: EMG force estimation, CNN-based model, raw EMG signals, frequency information.
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A Visual Search System Powered by Locality Sensitive Hashing
Abstract: This paper presents an end-to-end large―scale visual search system. Several challenges were met during its development: dealing with heptamerous image data, indexing large―scale images for massive data updating, training deep learning models for effective feature representation without a lot of manual work, improving the latency and providing accurate results. We assessed our system on a publicly available dataset consisting of flowers commonly available in the UK. This paper describes our implementation in great details as well as our lessons learnt during the building of such a large-scale system. We used locality sensitive hashing to perform the nearest neighbor search and given the data size, we relied on the random projection technique. In order to represent the image in a smaller dimension we used Bit-ResNet as the underlying model given its fine-tuning setup. Extensive experiments show that our system provides satisfactory results.
Keywords: Additional Key Words and Phrases: datasets, neural networks, visual search, locality sensitive hashing
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Emergence of Equal Cooperation Induced by Characteristics of Adolescence than Adulthood in an Interrole Conflict Game among Reinforcement Learning Agents
Abstract: People usually belong to multiple groups, and in such situations “interrole conflicts”occur. Studies on interrole conflicts have mainly targeted subjects after adulthood, although they do not occur only after the developmental stage of adulthood. In addition, limited studies have been conducted on interrole conflicts during adolescence. Furthermore, simulation studies about interrole conflicts have rarely been conducted. The purpose of this study is to clarify the difference between adolescence and adulthood in how to deal with interrole conflict situations. We propose an interrole conflict game (ICG) as a new game-theoretic framework to deal with interrole conflicts and adopt reinforcement learning agents with characteristics of adolescence or adulthood as players. Our multi-agent simulation (MAS) experiment results suggest high learning rate and low discount rate that cause typical adolescent characteristics including risk-taking, impulsivity and novelty seeking can be played important roles to cope an interrole conflict and for emergence of equal cooperation among adolescents in the interrole conflict situation.
Keywords: interrole conflict game (ICG), reinforcement learning, multi-agent simulation (MAS), characteristics of adolescence or adulthood, emergence of equal cooperation.
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Drone Guidance System based Simultaneous Localization and Mapping for Free Parking Space Localization
Abstract:The rapid development of drone has changed the way of our lifestyle by helping us to deal with many issues we were unable to solve before. Such as scanning a large farm area for tracking boars using the drone integrated CMOS camera and so on. In this research, we built a map for drone guidance system in order to detect a fee parking space. The Simultaneous Localization and Mapping (SLAM) is used as a control method. “SLAM”, is the process of mapping an area whilst keeping track of the location of the device within that area. A technology that enables autonomous flight even in an environment where GPS cannot be used . In addition, we used a Versatile and Accurate Monocular SLAM (ORB-SLAM) for real time operation and Large-Scale Direct Monocular Simultaneous Localization and Mapping (LSD-SLAM) which are typical SLAMs based visual SLAM. The experimental result shows that, although the generated map was somehow difficult to visualized but due the camera’s self-position estimation gave a rough route of the path which enable the drone to locate the free parking lots. Our future focus will be to implement the automatic flying system based on the generated map and the improvement of the ORB-SLAM features.
Keywords: slam; cmos camera ; orb-slam, parking lots; localization, guidance system
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New Image Processing Application for Life Signs Detection
Abstract: Natural disasters such as earthquakes, landslides and tornados, occur quickly and unexpectedly leaving no time to prepare for rescue, thus causing loss of lives. Unlike the types of disasters mentioned above, this research focuses on disasters such as typhoons and tsunamis, where there is little time to prepare for the worst cases when people are stuck in their home waiting for the Search and Rescue Team (SRT). In most cases, after a disaster occurs the SRT are often unable to quickly search and rescue those who need help. This is due to many reasons. One of the most important reasons is the lack of a practical and efficient rescue system available for the rescue task. To overcome this problem, this research proposes a practical, comprehensive and efficient new search scheme to quickly detect and rescue people and facilitate the SRT task. In this new scheme a person waiting for the SRT, will post outdoors a fife sign which can be made with any familiar household items. The sign should not coincide with any existing outside signs. Once the SRT arrives on the disaster site, each drone is launched at the search spot, based on the damage area and city map information integrated into the drones. The drones mission is to automatically and quickly identify and find signs of life. This new search and rescue scheme is developed on the basis of high image processing technology. This current article proposes a rescue sign for the task.
Keywords: Pattern recognition, rescue team, disaster, search, image processing, search and rescue
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Changes in the Perception of Postoperative Delirium Before and After a Simulated Experience of Postoperative Delirium in Nursing Students
Abstract: The purpose of this study is to use Unity to recreate the hallucinations and auditory
hallucinations experienced by patients who develop delirium, and to use VR to reveal the changes in perception of delirium before and after the simulated experience of delirium to nursing college students. We used VR to recreate the ICU at night and created a video of a simulated postoperative delirium experience. The duration is 12 minutes. We set up scenes of cockroaches appearing on the ceiling, the ceiling closing in on them, people in protective clothing, and soldiers attacking at two-minute intervals. Oculus Quest was used as the head-mounted display (HMD) for viewing the VR images. The target participants were 17 students in the second to the fourth year of nursing college. The target students were asked to answer two questions before and after the viewing. The interview content was analyzed by comparing the differences in speaking time, amount of speech and content before and after the VR viewing for each student and by text mining. The results showed that using VR to simulate postoperative delirium can lead to a change in perception from understanding the inner life of patients with postoperative delirium. -
Geometric Shape Statistical Analysis of Tibial Plafond with Ankle Osteoarthritis
Abstract: The etiology of ankle osteoarthritis is not enough elucidated, and similar to hip and knee
osteoarthritis, the ankle osteoarthritis caused cartilage loss and pain in daily life. However, the ankle osteoarthritis has a worse prognosis than the hip or the knee osteoarthritis. Considering the ankle osteoarthritis skeletal structure is important in selecting the appropriate surgery for the patient. Selecting the appropriate surgery will lead to an improved prognosis. X-ray images and Computed Tomography (CT) scan images are usually used to classify ankle osteoarthritis, but the evaluation of 3D bone structure is difficult and the classification based on two-dimensional measurements of X-ray and CT images may vary among medical doctors. The purpose of this study is to investigate the three-dimensional geometric deformation characteristics of tibial plafond by using statistical analysis. Deformation characteristics were found in high severity ankle osteoarthritis compared with mild group. In particular, there was a statistically significant difference in the varus deformity of the articular facet of medial malleolus and tibial plafond between stage 3B and mild group (p < 0.006), and the hyperostosis of the medial malleolus between stage 4 and mild group (p < 0.002). These results suggest that patients with severe ankle osteoarthritis have common deformity characteristics in the tibial plafond. On the contrary, there was no significant difference in the deformity of the edge of anterior and posterior tibial plafond. Such phenomena suggest that the edge of anterior and posterior tibial plafond deforms regardless of ankle osteoarthritis. This study contributes to the scientific advancement of ankle osteoarthritis surgery.