A breathing circuit alarm system based on neural networks.
OBJECTIVE
The objectives of our study were (1) to implement intelligent respiratory alarms with a neural network; and (2) to increase alarm specificity and decrease false-alarm rates compared with current alarms.


METHODS
We trained a neural network to recognize 13 faults in an anesthesia breathing circuit. The system extracted 30 breath-to-breath features from the airway CO2, flow, and pressure signals. We created training data for the network by introducing 13 faults repeatedly in 5 dogs (616 total faults). We used the data to train the neural network using the backward error propagation algorithm.


RESULTS
In animals, the trained network reported the alarms correctly for 95.0% of the faults when tested during controlled ventilation, and for 86.9% of the faults during spontaneous breathing. When tested in the operating room, the system found and correctly reported 54 of 57 faults that occurred during 43.6 hr of use. The alarm system produced a total of 74 false alarms during 43.6 hr of monitoring.


CONCLUSIONS
Neural networks may be useful in creating intelligent anesthesia alarm systems.
Orr
J A
JA
Department of Anesthesiology, University of Utah, Salt Lake City 84132.
Westenskow
D R
DR
D000776:Anesthesiology / Q000295:instrumentation*; D000818:Animals; D004867:Equipment Design; D006801:Humans; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D016571:Neural Networks, Computer; D012137:Respiratory System