TY  - JOUR 
AU  - Alvarez, Marcos Lazaro
AU  - Arjona, Laura
AU  - Iglesias Martínez, Miguel E.
AU  - Bahillo Martínez, Alfonso
PY  - 2024
SN  - 1687-4722
UR  - https://uvadoc.uva.es/handle/10324/80195
AB  - This work constitutes the first approach for automatically classifying the surface that the voiding flow impacts in non-invasive sound uroflowmetry tests using machine learning. Often, the voiding flow impacts the toilet walls (traditionally made of...
LA  - eng
PB  - Springer Nature
KW  - Sound uroflowmetry
KW  - Machine learning
KW  - Automatic classification
KW  - Surface automatic classification
KW  - Acoustic  voiding signals
TI  - Automatic classification of the physical surface in sound uroflowmetry using machine learning methods
DO  - 10.1186/S13636-024-00332-Y
ER  -