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

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

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 p...

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Bibliographic Details
Published in:Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) Vol. 6; pp. 3530 - 3533 vol.6
Main Authors: Rayburn, D.B., Fitzpatrick, T.M., Van Albert, S.A.
Format: Conference Proceeding
Language:English
Published: IEEE 1994
Subjects:
Biomedical engineering
Biomedical measurements
Carbon dioxide
Gases
Graphics
Neural networks
Patient monitoring
Testing
Ventilation
Volume measurement
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Summary: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.< >
ISBN:078031901X
9780780319011
DOI:10.1109/ICNN.1994.374903