DocumentCode :
3598171
Title :
Neural Network Models for Biosignal Analysis
Author :
Cohen, Maurice E. ; Hudson, Donna L.
Author_Institution :
California Univ., San Francisco, CA
fYear :
2006
Firstpage :
3537
Lastpage :
3540
Abstract :
Signal analysis provides important clues for diagnosis of disease in many arenas, particularly in cardiology. While each result may give good diagnostic information, a comprehensive decision model requires the combination of results. In the work described here, a neural network model is used to combine various features obtained through signal analysis. The original model is based on electrocardiogram (ECG analysis). This model is extended in two ways: the neural network model is expanded to include other clinical parameters in addition to the ECG and the method is generalized to include other biosignals
Keywords :
diseases; electrocardiography; medical signal processing; neural nets; patient diagnosis; ECG analysis; biosignal analysis; cardiology; clinical parameters; diagnostic information; disease diagnosis; electrocardiogram; neural network model; Cardiac disease; Cardiology; Cardiovascular diseases; Cities and towns; Electrocardiography; Information analysis; Neural networks; Patient monitoring; Signal analysis; USA Councils; Nonlinear analysis; biosignals; chaos theory; neural network modeling; symbolic processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
ISSN :
1557-170X
Print_ISBN :
1-4244-0032-5
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2006.260393
Filename :
4462560
Link To Document :
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