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...
详细信息
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...
详细信息
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...
详细信息
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...
详细信息
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...
详细信息
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.
暂无评论