Neural network evaluation of slopes from sequential volume segments of expiratory carbon dioxide curves

Author

Rayburn, Daniel B. ; Fitzpatrick, Thomas M. ; Van Albert, Stephen A.

Author_Institution

Dept. of Respiratory Res., Walter Reed Army Inst. of Res., Washington, DC, USA

Volume

6

fYear

1994

fDate

27 Jun- 2 Jul 1994

Firstpage

3530

Abstract

Capnography is currently used to evaluate respiratory efficiency in monitored patients with indirect indications of alveolar dead space and the distribution of ventilation perfusion ratios. However, it has not been associated with the typical spirometric values and associated pulmonary obstructive processes. The purpose of this study was to determine if specific segments of the capnogram could be more closely associated with the status of airway obstruction. Two fully connected ANNs were used to compute forced expiratory volume and forced vital capacity

Keywords

lung; medical computing; neural nets; airway obstruction; alveolar dead space; capnography; expiratory carbon dioxide curves; forced expiratory volume; forced vital capacity; neural network evaluation; sequential volume segments; ventilation perfusion ratios; Biomedical engineering; Biomedical measurements; Carbon dioxide; Gases; Graphics; Neural networks; Patient monitoring; Testing; Ventilation; Volume measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374903

Filename

374903