Title :
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
fDate :
27 Jun- 2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374903
