Analysis of multiple linear regression algorithms used for respiratory mechanics monitoring during artificial ventilation

Many patients undergo long-term artificial ventilation and their respiratory system mechanics should be monitored to detect changes in the patient's state and to optimize ventilator settings. In this work the most popular algorithms for tracking variations of respiratory resistance ( R rs ) and...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computer methods and programs in biomedicine Ročník 101; číslo 2; s. 126 - 134
Hlavní autor: Polak, Adam G.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Ireland Elsevier Ireland Ltd 01.02.2011
Témata:
Accuracy analysis
Airflow
Algorithms
Artificial ventilation
Computer programs
Forward-inverse modelling
Humans
Internal Medicine
Lungs
Mathematical models
Multiple linear regression
Noise
Other
Patients
Respiration, Artificial
Respiratory Mechanics
Respiratory mechanics monitoring
Ventilation
Respiratory mechanics monitoring
Artificial ventilation
Multiple linear regression
Accuracy analysis
Forward-inverse modelling
ISSN:0169-2607, 1872-7565, 1872-7565
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Many patients undergo long-term artificial ventilation and their respiratory system mechanics should be monitored to detect changes in the patient's state and to optimize ventilator settings. In this work the most popular algorithms for tracking variations of respiratory resistance ( R rs ) and elastance ( E rs ) over a ventilatory cycle were analysed in terms of systematic and random errors. Additionally, a new approach was proposed and compared to the previous ones. It takes into account an exact description of flow integration by volume-dependent lung compliance. The results of analyses showed advantages of this new approach and enabled to form several suggestions. Algorithms including R rs and E rs dependencies on airflow and lung volume can be effectively applied only at low levels of noise present in measurement data, otherwise the use of the simplest model with constant parameters is preferable. Additionally, one should avoid including the resistance dependence on airflow alone, since this considerably destroys the retrieved trace of R rs . Finally, the estimated cyclic trajectories of R rs and E rs are more sensitive to noise present in pressure than in the flow signal, and the elastance traces are estimated more accurately than the resistance ones.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0169-2607
1872-7565
1872-7565
DOI:10.1016/j.cmpb.2010.08.001