DocumentCode :
3726637
Title :
Automatic Diagnosis of Voiding Dysfunction From Sound Signal
Author :
Hurt?k;Michal Burda;Jan Krhut;Peter Zvara; Lun?cek
Author_Institution :
Inst. for Res. &
fYear :
2015
Firstpage :
1331
Lastpage :
1336
Abstract :
The aim of this paper is to present the results of an experiment towards Sonouroflowmetry, a novel approach for recognition of potential voiding dysfunctions based on machine learning classification of sound records that are obtained while a patient urinates into water in a toilet bowl. Such approach could enable a diagnosis of the voiding dysfunctions via a mobile device. We provide a comparison of 69 state-of-the-art classification methods.
Keywords :
"Feature extraction","Time measurement","Testing","Cellular phones","Mobile handsets","Bladder","Pathology"
Publisher :
ieee
Conference_Titel :
Computational Intelligence, 2015 IEEE Symposium Series on
Print_ISBN :
978-1-4799-7560-0
Type :
conf
DOI :
10.1109/SSCI.2015.190
Filename :
7376766
Link To Document :
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