Leveraging Synthetic Data to Develop a Machine Learning Model for Voiding Flow Rate Prediction From Audio Signals

Leveraging Synthetic Data to Develop a Machine Learning Model for Voiding Flow Rate Prediction From Audio Signals

Sound-based uroflowmetry (SU) is a non-invasive technique emerging as an alternative to traditional uroflowmetry (UF) to calculate the voiding flow rate based on the sound generated by the urine impacting the water in a toilet, enabling remote monitoring and reducing the patient burden and clinical...

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Bibliographic Details
Main Authors: Marcos Lazaro Alvarez, Alfonso Bahillo, Laura Arjona, Diogo Marcelo Nogueira, Elsa Ferreira Gomes, Alipio M. Jorge
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Machine learning
non-invasive voiding monitoring
sound-based uroflowmetry
sound voiding signals
voiding flow estimation
Online Access:https://ieeexplore.ieee.org/document/11084787/
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