A novel estimation methodology for tracheal pressure in mechanical ventilation control

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Ajcevic:2013:ISPA,

• author = "Milos Ajcevic and Andrea {De Lorenzo} and Agostino Accardo and Alberto Bartoli and Eric Medvet",
• booktitle = "8th International Symposium on Image and Signal Processing and Analysis (ISPA 2013)",
• title = "A novel estimation methodology for tracheal pressure in mechanical ventilation control",
• year = "2013",
• month = "4-6 " # sep,
• address = "Trieste, Italy",
• pages = "695--699",
• 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",
• DOI = "doi:10.1109/ISPA.2013.6703827",
• size = "5 pages",
• 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.",
• notes = "Also known as \cite{6703827}",

}

Genetic Programming entries for Milos Ajcevic Andrea De Lorenzo Agostino Accardo Alberto Bartoli Eric Medvet

Citations