title: Segmentation of respiratory signals by evidence theory.
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.
authors: Strasbourg University, LSIIT - UMR CNRS 7005, France.
authors: Akram
authors: A
authors: Belghith
authors: Christophe
authors: C
authors: Collet
mesh_terms: D000704:Analysis of Variance; D001314:Auscultation / Q000295:instrumentation / Q000379:methods; D003198:Computer Simulation; D004867:Equipment Design; D006801:Humans; D008962:Models, Theoretical; D011336:Probability; D012135:Respiratory Sounds / Q000502:physiology* / Q000503:physiopathology; D012680:Sensitivity and Specificity; D015398:Signal Transduction; D019023:Stethoscopes