DocumentCode :
2383730
Title :
Segmentation of respiratory signals by evidence theory
Author :
Belghith, Akram ; Collet, Christophe
Author_Institution :
LSIIT, Strasbourg Univ., Strasbourg, France
fYear :
2009
fDate :
3-6 Sept. 2009
Firstpage :
1905
Lastpage :
1908
Abstract :
This paper presents an evidential segmentation scheme of respiratory signals for the detection of the wheezing sounds. The segmentation is based on the modeling of the data by evidence theory which is well suited to represent such uncertain and imprecise data. In this paper, we particularly focus on the modelization of the data imprecision using the fuzzy theory. The modelization result is then used to define the mass function. The effectiveness of the method is demonstrated on synthetic and real signals.
Keywords :
acoustic signal detection; acoustic signal processing; bioacoustics; fuzzy set theory; medical signal detection; medical signal processing; pneumodynamics; evidence theory; fuzzy theory; mass function definition; respiratory signal segmentation; wheezing sound detection; Data fusion; evidence theory; fuzzy membership function; imprecision; segmentation; Analysis of Variance; Auscultation; Computer Simulation; Equipment Design; Humans; Models, Theoretical; Probability; Respiratory Sounds; Sensitivity and Specificity; Signal Transduction; Stethoscopes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
Conference_Location :
Minneapolis, MN
ISSN :
1557-170X
Print_ISBN :
978-1-4244-3296-7
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2009.5333026
Filename :
5333026
Link To Document :
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