Artificial neural network cardiopulmonary modeling and diagnosis

Kangas, L J; Keller, P E

Title: Artificial neural network cardiopulmonary modeling and diagnosis

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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. 12 figs.

Inventors:: Kangas, L J; Keller, P E

Issue Date:: 1997-10-28

Research Org.:: Battelle Memorial Institute, Columbus, OH (United States)

Sponsoring Org.:: USDOE, Washington, DC (United States)

OSTI Identifier:: 541740

Patent Number(s):: 5680866

Application Number:: PAN: 8-625,025

Assignee:: Battelle Memorial Inst., Richland, WA (United States)

DOE Contract Number:: AC06-76RL01830

Resource Type:: Patent

Resource Relation:: Other Information: PBD: 28 Oct 1997

Country of Publication:: United States

Language:: English

Subject:: 55 BIOLOGY AND MEDICINE, BASIC STUDIES; CARDIOVASCULAR DISEASES; NEURAL NETWORKS; DIAGNOSTIC TECHNIQUES; DIAGNOSIS; MULTIVARIATE ANALYSIS; MEDICINE

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                    Kangas, L J, and Keller, P E. Artificial neural network cardiopulmonary modeling and diagnosis.  United States: N. p., 1997. 
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                    Kangas, L J, & Keller, P E. Artificial neural network cardiopulmonary modeling and diagnosis.  United States.

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                    Kangas, L J, and Keller, P E. Tue .  
        "Artificial neural network cardiopulmonary modeling and diagnosis".  United States.

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@article{osti_541740,

  title        = {Artificial neural network cardiopulmonary modeling and diagnosis},

  author       = {Kangas, L J and Keller, P 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. 12 figs.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {1997},

  month        = {10}

}

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