Objective Prompt, accurate, objective assessment of concussion is crucial as delays can lead to increased short and long-term consequences. The purpose of this study was to derive an objective multimodal concussion i...
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Objective Prompt, accurate, objective assessment of concussion is crucial as delays can lead to increased short and long-term consequences. The purpose of this study was to derive an objective multimodal concussion index (CI) using EEG at its core, to identify concussion, and to assess change over time throughout recovery. Methods Male and female concussed (N = 232) and control (N = 206) subjects 13–25 years were enrolled at 12 US colleges and high schools. Evaluations occurred within 72 h of injury, 5 days post-injury, at return-to-play (RTP), 45 days after RTP (RTP + 45); and included EEG, neurocognitive performance, and standard concussion assessments. Concussed subjects had a witnessed head impact, were removed from play for ≥ 5 days using site guidelines, and were divided into those with RTP < 14 or ≥14 days. Part 1 describes the derivation and efficacy of the machine learning derived classifier as a marker of concussion. Part 2 describes significance of differences in CI between groups at each time point and within each group across time points. Results Sensitivity = 84.9%, specificity = 76.0%, and AUC = 0.89 were obtained on a test Hold-Out group representing 20% of the total dataset. EEG features reflecting connectivity between brain regions contributed most to the CI. CI was stable over time in controls. Significant differences in CI between controls and concussed subjects were found at time of injury, with no significant differences at RTP and RTP + 45. Within the concussed, differences in rate of recovery were seen. Conclusions The CI was shown to have high accuracy as a marker of likelihood of concussion. Stability of CI in controls supports reliable interpretation of CI change in concussed subjects. Objective identification of the presence of concussion and assessment of readiness to return to normal activity can be aided by use of the CI, a rapidly obtained, point of care assessment tool.
An automated system for resolving an intramuscular electromyographic (EMG) signal into its constituent motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where se...
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ISBN:
(纸本)9781424441198
An automated system for resolving an intramuscular electromyographic (EMG) signal into its constituent motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where several physiological parameters for each motor unit (MU), such as the motor unit potential (MUP) template and mean firing rate, are required. The system decomposes an EMG signal off-line by filtering the signal, detecting MUPs, and then grouping the detected MUPs using a clustering and a supervised classification algorithm. Both the clustering and supervised classification algorithms use MUP shape and MU firing pattern information to group MUPs into several MUPTs. Clustering is partially based on the K-means clustering algorithm. Supervised classification is implemented using a certainty-based classifier technique that employs a knowledge-based system to merge trains, detect and correct invalid trains, as well as adjust the assignment threshold for each train. The accuracy (93.2%±5.5%), assignment rate(93.9%±2.6%), and error in estimating the number of MUPTs(0.3±0.5) achieved for 10 simulated EMG signals comprised of 3- 11 MUPTs are encouraging for using the system for decomposing various EMG signals.
Big data is the large set of dataset. It involves extraction, selection, analyzing and interpolation of data. Big data is used wide assortment in medical fields for analyzing the patient's medical history, predict...
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ISBN:
(纸本)9781509049295
Big data is the large set of dataset. It involves extraction, selection, analyzing and interpolation of data. Big data is used wide assortment in medical fields for analyzing the patient's medical history, prediction of future effects and clinical decision making. It can also be used as a tool to store large number of data. It helps us to understand the diseases and also paves way to predict the disease and its future effects caused by the disease. In this paper we use RBFNN (Radial Basis Function Neural Network) with classifier algorithm with the use of parameters to determine the condition of a patient as a normal or a kidney failure patient. The proposed method reveals the stages of the kidney failure patient and treatment and clinical decision.
Data Mining classification task is categorized as a part of knowledge acquisition process, which can be implemented through the analysis procedure in related databases. In this study, we aimed to employ this technique...
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ISBN:
(纸本)9781612842127
Data Mining classification task is categorized as a part of knowledge acquisition process, which can be implemented through the analysis procedure in related databases. In this study, we aimed to employ this technique to perform talent knowledge acquisition process in Human Resource (HR) by using talent databases. In HR, among the challenges of HR professionals is to manage organization's talents, especially to ensure the right person assign to the right job at the right time. In this case, knowledge discovered from talent knowledge acquisition process can be used by professionals in HR to handle various tasks in talent management. In this article, we present an experimental study to identify the potential data mining classification technique for talent knowledge acquisition. Talent knowledge discovered from related databases can be used to classify the appropriate talent among employees. In experimental phase, we used selected classification algorithms in order to propose the suitable classifier from talent datasets. As a result, the C4.5 classifier algorithm from decision tree family is recommended as a suitable classifier for the datasets. Classification model performed by this classifier can be used in talent management especially for talent classification or prediction.
In this article we have presented a model used for a classification of multidimensional data in a broader sense, called Braun's cathode machine. The internal structure of the machine presented on this paper has be...
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ISBN:
(纸本)9783642106828
In this article we have presented a model used for a classification of multidimensional data in a broader sense, called Braun's cathode machine. The internal structure of the machine presented on this paper has been based on the architecture of a cathode-ray tube Braun's tube. For a machine model described this way a machine training algorithm has been proposed as well as response computing algorithms. In the final chapter we have presented the results of the machine tests for the notions connected with the classification and self-organization of multidimensional data.
In this paper, based on the discussion of some important issues related to cooperative design, and the analysis for niche technology, a group classifier algorithm and a sharing learning algorithm in a multi-agent coop...
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ISBN:
(纸本)9781424435340
In this paper, based on the discussion of some important issues related to cooperative design, and the analysis for niche technology, a group classifier algorithm and a sharing learning algorithm in a multi-agent cooperative system are put forward. The aim is to use socio-cultural perspectives and niche technology for supporting design reuse and share in a cooperative design system.
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