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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:dateCreated |
1996-3-4
|
| pubmed:abstractText |
A competent breathing circuit is mandatory to the safe and effective delivery of oxygen and anesthetic gases to the patient. Studies have shown that failures in the circuit are the most likely causes of anesthetic mishaps. Unfortunately, the complexity of the system renders traditional monitoring methods ineffective. We have developed a hierarchical artificial neural network monitor that is capable of examining ventilator signals. It was trained to identify 23 faults in the breathing circuit during ventilator controlled breathing and 21 faults during spontaneous breathing. The networks correctly identified a fault condition in 92% and 83% of cases for ventilator and spontaneous data, respectively. The correct fault type was found in 76% and 68% of cases for ventilator and spontaneous data, respectively. Results show that the network met our criteria for a holistic, specific, and vigilant monitoring system.
|
| pubmed:commentsCorrections | |
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
|
| pubmed:issn |
0195-4210
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
96-100
|
| pubmed:dateRevised |
2009-11-18
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| pubmed:meshHeading | |
| pubmed:year |
1995
|
| pubmed:articleTitle |
Intelligent monitor for an anesthesia breathing circuit.
|
| pubmed:affiliation |
Anesthesiology Bioengineering Laboratory, University of Utah, Salt Lake City, USA.
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| pubmed:publicationType |
Journal Article
|