ECG sensor signal analysis to represent cases in a case-based stress diagnosis system

2010 ◽  
Author(s):  
S. Begum ◽  
M. U. Ahmed ◽  
P. Funk
Keyword(s):  
Signal Analysis ◽  
Sensor Signal ◽  
Diagnosis System ◽  
Case Based

scholarly journals Sensor, Signal and Image Informatics

2006 ◽  
Vol 15 (01) ◽  
pp. 57-67 ◽  
Author(s):  
T. Aach ◽  
H. Witte ◽  
T.M. Lehmann
Keyword(s):  
Data Acquisition ◽  
Signal Analysis ◽  
Emission Computed Tomography ◽  
Biomedical Data ◽  
Data Handling ◽  
Sensor Signal ◽  
Biomedical Signal ◽  
Time Frequency ◽  
Image Informatics ◽  
Recent Trends

SummaryThe number of articles published annually in the fields of biomedical signal and image acquisition and processing is increasing. Based on selected examples, this survey aims at comprehensively demonstrating the recent trends and developments.Four articles are selected for biomedical data acquisition covering topics such as dose saving in CT, C-arm X-ray imaging systems for volume imaging, and the replacement of dose-intensive CTbased diagnostic with harmonic ultrasound imaging. Regarding biomedical signal analysis (BSA), the four selected articles discuss the equivalence of different time-frequency approaches for signal analysis, an application to Cochlea implants, where time-frequency analysis is applied for controlling the replacement system, recent trends for fusion of different modalities, and the role of BSA as part of a brain machine interfaces. To cover the broad spectrum of publications in the field of biomedical image processing, six papers are focused. Important topics are content-based image retrieval in medical applications, automatic classification of tongue photographs from traditional Chinese medicine, brain perfusion analysis in single photon emission computed tomography (SPECT), model-based visualization of vascular trees, and virtual surgery, where enhanced visualization and haptic feedback techniques are combined with a sphere-filled model of the organ.The selected papers emphasize the five fields forming the chain of biomedical data processing: (1) data acquisition, (2) data reconstruction and pre-processing, (3) data handling, (4) data analysis, and (5) data visualization. Fields 1 and 2 form the sensor informatics, while fields 2 to 5 form signal or image informatics with respect to the nature of the data considered.Biomedical data acquisition and pre-processing, as well as data handling, analysis and visualization aims at providing reliable tools for decision support that improve the quality of health care. Comprehensive evaluation of the processing methods and their reliable integration in routine applications are future challenges in the field of sensor, signal and image informatics.

Coordination Fault Diagnosis Based on RBF

2013 ◽  
Vol 329 ◽  
pp. 278-282
Author(s):  
Rui Hua Xu ◽  
Zheng Zhou Wang ◽  
Ya Dong Yan ◽  
Cai Wen Ma
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
High Accuracy ◽  
Case Based Reasoning ◽  
Rule Based ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Simulation Results ◽  
Self Learning ◽  
Case Based

In large-scale complex system, The establishment of a fast, accurate fault diagnosis system is more difficult because there exist many uncertain elements between the fault cause and the fault sign .A fault diagnosis system is established based on RBF cloud neural network ,the RBR (rule-based reasoning) and the CBR (case-based reasoning).The fault diagnosis system not only has the advantages of self-learning, high accuracy, randomness, fuzziness, etc ,and has the advantages of independently of mathematical model ,rich knowledge representation, mighty problem solving ability, etc. Theoretical analysis and simulation results show that the system is feasible and effective for fast and accurate fault positioning of complex systems.

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