Artificial neural network cardiopulmonary modeling and diagnosis
Abstract
The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.
- Inventors:
-
- Richland, WA
- Issue Date:
- Research Org.:
- Pacific Northwest National Laboratory (PNNL), Richland, WA
- OSTI Identifier:
- 871203
- Patent Number(s):
- 5680866
- Assignee:
- Battelle Memorial Institute (Richland, WA)
- DOE Contract Number:
- AC06-76RL01830
- Resource Type:
- Patent
- Country of Publication:
- United States
- Language:
- English
- Subject:
- artificial; neural; network; cardiopulmonary; modeling; diagnosis; method; diagnosing; condition; individual; comparing; data; progressive; multi-stage; non-linear; multi-variate; model; preferably; recurrent; sensor; fusion; relies; cardiovascular; developed; physiological; measurements; differences; modeled; parameters; time; artificial neural; neural network; neural net; /600/706/
Citation Formats
Kangas, Lars J, and Keller, Paul E. Artificial neural network cardiopulmonary modeling and diagnosis. United States: N. p., 1997.
Web.
Kangas, Lars J, & Keller, Paul E. Artificial neural network cardiopulmonary modeling and diagnosis. United States.
Kangas, Lars J, and Keller, Paul E. Wed .
"Artificial neural network cardiopulmonary modeling and diagnosis". United States. https://www.osti.gov/servlets/purl/871203.
@article{osti_871203,
title = {Artificial neural network cardiopulmonary modeling and diagnosis},
author = {Kangas, Lars J and Keller, Paul E},
abstractNote = {The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1997},
month = {1}
}
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