DocumentCode :
2444189
Title :
Breath flow sensing via spirometric instrumentation: Pathology prediction using a genetic algorithm
Author :
Lay-Ekuakille, A. ; Vendramin, G. ; Trotta, A.
Author_Institution :
Dipt. d´´Ing. del´´Innovazione, Univ. of Salento, Lecce
fYear :
2008
fDate :
Nov. 30 2008-Dec. 3 2008
Firstpage :
313
Lastpage :
317
Abstract :
Spirometry takes care to find and to predict respiratory system pathologies through instrumentation that mainly carries out measurements on the volume and the air flow expired from lungs. A complete spirometric instrumentation composed of three parts has been developed. The first part, ldquohardwarerdquo, gains a sampled signal from a sensor of the flow-time curve and sends it to the computer. The second part, ldquosoftwarerdquo, processes received data calculating the volume-time curve, the flow-volume curve and other main spirometric parameters, displaying the result of prediction. The last part, ldquoa genetic algorithmrdquo, trains itself on the base of a series of computing with real data, to produce spirometric parameters of a most likely pathologic curve and, to predict pathology type with less possible tests.
Keywords :
biosensors; flow sensors; genetic algorithms; pneumodynamics; breath flow sensing; flow-time curve; flow-volume curve; genetic algorithm; pathology prediction; respiratory system pathologies; spirometric instrumentation; spirometric parameters; volume-time curve; Spirometry; biomedical instrumentation; genetic algorithms; lung flux; respiratory pathology prediction; sensor;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Sensing Technology, 2008. ICST 2008. 3rd International Conference on
Conference_Location :
Tainan
Print_ISBN :
978-1-4244-2176-3
Electronic_ISBN :
978-1-4244-2177-0
Type :
conf
DOI :
10.1109/ICSENST.2008.4757120
Filename :
4757120
Link To Document :
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