A novel estimation methodology for tracheal pressure in mechanical ventilation control
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Ajcevic:2013:ISPA,
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author = "Milos Ajcevic and Andrea {De Lorenzo} and
Agostino Accardo and Alberto Bartoli and Eric Medvet",
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booktitle = "8th International Symposium on Image and Signal
Processing and Analysis (ISPA 2013)",
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title = "A novel estimation methodology for tracheal pressure
in mechanical ventilation control",
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year = "2013",
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month = "4-6 " # sep,
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address = "Trieste, Italy",
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pages = "695--699",
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keywords = "genetic algorithms, genetic programming, biomechanics,
biomedical electronics, biomedical equipment, diseases,
injuries, medical control systems, patient treatment,
physiological models, air flow pressure, air flow
properties, barotrauma, endotracheal tubes, estimation
methodology, high-frequency percussive ventilation,
mechanical ventilation control, nonconventional
mechanical ventilatory strategy, pathological
conditions, patient airway, patient treatment,
state-of-the-art baseline models, tracheal pressure,
ventilator circuit, volutrauma, Electron tubes, Lungs,
Physiology, Pressure measurement, Testing,
Ventilation",
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DOI = "doi:10.1109/ISPA.2013.6703827",
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size = "5 pages",
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abstract = "High-frequency percussive ventilation (HFPV) is a
non-conventional mechanical ventilatory strategy which
has proved useful in the treatment of a number of
pathological conditions. HFPV usually involves the
usage of endotracheal tubes (EET) connecting the
ventilator circuit to the airway of the patient. The
pressure of the air flow insufflated by HFPV must be
controlled very accurately in order to avoid barotrauma
and volutrauma. Since the actual tracheal pressure
cannot be measured, a model for estimating such a
pressure based on the EET properties and on the air
flow properties that can actually be measured in
clinical practice is necessary. In this work we propose
a novel methodology, based on Genetic Programming, for
synthesising such a model. We experimentally evaluated
our models against the state-of-the-art baseline
models, crafted by human experts, and found that our
models for estimating tracheal pressure are
significantly more accurate.",
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notes = "Also known as \cite{6703827}",
- }
Genetic Programming entries for
Milos Ajcevic
Andrea De Lorenzo
Agostino Accardo
Alberto Bartoli
Eric Medvet
Citations