Integration of operating room monitors for development of a smart alarm system

مرکز و کتابخانه مطالعات اسلامی به زبان های اروپایی

منو

" Integration of operating room monitors for development of a smart alarm system "
M. J. Navabi-Shirazi K. C. Mylrea

Document Type	:	Latin Dissertation
Language of Document	:	English
Record Number	:	1112563
Doc. No	:	TLpq303820193
Main Entry	:	K. C. Mylrea
	:	M. J. Navabi-Shirazi
Title & Author	:	Integration of operating room monitors for development of a smart alarm system\ M. J. Navabi-ShiraziK. C. Mylrea
College	:	The University of Arizona
Date	:	1990
student score	:	1990
Degree	:	Ph.D.
Page No	:	96
Abstract	:	A computer based system was designed and used to collect physiologic and respiratory data (13 variables and 3 waveforms) from six routinely used operating room monitors. 23 hours of data were collected during 20 general surgery cases (ASA III patients). Part of the data were used to design and implement an integrated monitor with intelligent alarm capability. The system used a rule based approach to reduce false alarms and artificial neural networks (ANN) for classification of physiological waveforms. The integrated monitor was able to correctly identify 13 of 17 intubations which resulted in a 42% reduction in low end-tidal-CO2 false alarms. False heart rate alarms were reduced to 2.6% of total alarms using multi-variable analysis and rate of change limits. A combination of ANN's and an edge detection filter was used to classify CO2 waveforms into spontaneous, mechanical, and mechanical with spontaneous breathing attempts. The edge detection algorithm was able to detect 171 of 182 breaths. The ANN's properly classified 65 of 67 mechanical, 47 of 71 spontaneous, and 37 of 44 mechanical breaths with spontaneous breathing attempts. Another ANN was used for detection of elevated and depressed ST segments in the ECG signal. All ST segment elevations and depressions of 0.1 mV were correctly identified. An attempt was made to use ANN's to classify ECG waveforms according to anesthetic levels. However, the back-propagation algorithm used to train the network did not converge perhaps due to the variety of drugs used in the different cases. The system met our goals of providing an integrated operating room monitor with intelligent alarm capability. The system significantly reduced false heart rate alarms, detected intubation and classified ECG and CO2 waveforms.
Subject	:	alarm system
	:	Applied sciences
	:	Artificial intelligence
	:	Biomedical research
	:	Electrical engineering