Automatic diagnosis of voiding dysfunction from sound signal

Petr Hurtik; Michal Burda; Jan Krhut; Peter Zvara; Libor Lunacek

doi:10.1109/SSCI.2015.190

Automatic diagnosis of voiding dysfunction from sound signal

Petr Hurtik, Michal Burda, Jan Krhut, Peter Zvara, Libor Lunacek

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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.

Original language	English
Title of host publication	Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015
Publisher	IEEE
Publication date	2015
Pages	1331-1336
Article number	7376766
ISBN (Electronic)	9781479975600
DOIs	https://doi.org/10.1109/SSCI.2015.190
Publication status	Published - 2015
Externally published	Yes
Event	IEEE Symposium Series on Computational Intelligence, SSCI 2015 - Cape Town, South Africa Duration: 8. Dec 2015 → 10. Dec 2015

Conference

Conference	IEEE Symposium Series on Computational Intelligence, SSCI 2015
Country/Territory	South Africa
City	Cape Town
Period	08/12/2015 → 10/12/2015

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Documents & Links

10.1109/SSCI.2015.190

Cite this

@inproceedings{bb0eacf2a68a4767893dcc72267b17c9,

title = "Automatic diagnosis of voiding dysfunction from sound signal",

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.",

author = "Petr Hurtik and Michal Burda and Jan Krhut and Peter Zvara and Libor Lunacek",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Symposium Series on Computational Intelligence, SSCI 2015 ; Conference date: 08-12-2015 Through 10-12-2015",

year = "2015",

doi = "10.1109/SSCI.2015.190",

language = "English",

pages = "1331--1336",

booktitle = "Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015",

publisher = "IEEE",

address = "United States",

}

Hurtik, P, Burda, M, Krhut, J , Zvara, P & Lunacek, L 2015, Automatic diagnosis of voiding dysfunction from sound signal. in Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015., 7376766, IEEE, pp. 1331-1336, IEEE Symposium Series on Computational Intelligence, SSCI 2015, Cape Town, South Africa, 08/12/2015. https://doi.org/10.1109/SSCI.2015.190

TY - GEN

T1 - Automatic diagnosis of voiding dysfunction from sound signal

AU - Hurtik, Petr

AU - Burda, Michal

AU - Krhut, Jan

AU - Zvara, Peter

AU - Lunacek, Libor

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

U2 - 10.1109/SSCI.2015.190

DO - 10.1109/SSCI.2015.190

M3 - Article in proceedings

AN - SCOPUS:84964937663

SP - 1331

EP - 1336

BT - Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015

PB - IEEE

T2 - IEEE Symposium Series on Computational Intelligence, SSCI 2015

Y2 - 8 December 2015 through 10 December 2015

ER -

Automatic diagnosis of voiding dysfunction from sound signal

Abstract

Conference

Bibliographical note

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Cite this