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Automated Estimation and Analysis of Lung Function Test Parameters from Spirometric Data for Respiratory Disease Diagnostics
Author(s) -
Aruneema Das,
DP Johns,
Ritaban Dutta,
E. Haydn Walters
Publication year - 2014
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2014.05.188
Subject(s) - spirometer , computer science , pulmonary function testing , graphical user interface , software , spirometry , raw data , regression analysis , interface (matter) , data mining , asthma , machine learning , medicine , radiology , exhaled nitric oxide , bubble , maximum bubble pressure method , parallel computing , programming language
A spirometer is used for basic lung function test for preliminary diagnosis of respiratory diseases. There are significant amount of calculations and graphical analysis required to transform the raw spirometric data into meaningful parameters. This parameters and graphs help the physicians in preliminary patient diagnosis for respiratory disorders like asthma, chronic obstructive pulmonary disease, etc. This study was undertaken for the development of a software system which can be used with any spirometric instrument to automate the calculations of pulmonary dead space volumes and analysis of raw data. The clinician can feed the raw data from patient testing into the easy to use graphical user interface of the software which will be analyzed instantly and all the parameters, regression slopes, shape analysis plots and the results will be displayed graphically. The estimation of the vital parameters and regression slopes are based on standard protocols and equations. This system will eliminate presently practiced time consuming manual calculations and graphical analysis; will have increased precision, be considerably faster and more versatile

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