2026-04-27T20:15:23Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/801542025-12-19T13:37:11Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

Alvarez, Marcos Lazaro Bahillo Martínez, Alfonso Arjona, Laura Marcelo Nogueira, Diogo Ferreira Gomes, Elsa Jorge, Alípio M. 2025-11-28T12:26:42Z 2025-11-28T12:26:42Z 2025 IEEE Access, 2025, vol. 13, pp. 127240-127251 2169-3536 https://uvadoc.uva.es/handle/10324/80154 10.1109/ACCESS.2025.3590626 127240 127251 IEEE Access 13 2169-3536 Producción Científica 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 costs. This study trains four different machine learning (ML) models (random forest, gradient boosting, support vector machine and convolutional neural network) using both regression and classification approaches to predict and categorize the voiding flow rate from sound events. The models were trained with a dataset that contains sounds from synthetic void events generated with a high precision peristaltic pump and a traditional toilet. Sound was simultaneously recorded with three devices: Ultramic384k, Mi A1 smartphone and Oppo Smartwatch. To extract the audio features, our analysis showed that segmenting the audio signals into 1000 ms segments with frequencies up to 16 kHz provided the best results. Results show that random forest achieved the best performance in both regression and classification tasks, with a mean absolute error (MAE) of 0.9, 0.7 and 0.9 ml/s and quadratic weighted kappa (QWK) of 0.99, 1.0 and 1.0 for the three devices. To evaluate the models in a real environment and assess the effectiveness of training with synthetic data, the best-performing models were retrained and validated using a real voiding sounds dataset. The results reported an MAE below 2.5 ml/s and a QWK above 0.86 for regression and classification tasks, respectively. Ministerio de Ciencia, Innovación y Universidades (MICIU) a través del proyecto SWALU CPP2022-010045 2020 ‘‘Ayuda para contratos predoctorales’’ financiada por MICIU y la Agencia Estatal de Investigación (AEI), 10.13039/501100011033 y cofinanciada por el Fondo Social Europeo (FSE) bajo el lema ‘‘FSE invierte en tu futuro,’’ proyecto PRE2020-095612 Gobierno Vasco a través del Hazitek Program bajo el proyecto BATHMIC ZL-2024/00481 Ministerio a través del proyecto Aginplace financiado por MICIU, AEI/10.13039/501100011033 y por la Unión Europea (UE) a través del Fondo Europeo de Desarrollo Regional (FEDER), proyecto PID2023-146254OB-C41 y PID2023-146254OA-C44 application/pdf eng IEEE info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2025 The Authors Atribución 4.0 Internacional Machine learning non-invasive voiding monitoring sound-based uroflowmetry sound voiding signals voiding flow estimation 3311.10 Instrumentos Médicos 1203 Ciencia de Los Ordenadores Leveraging Synthetic Data to Develop a Machine Learning Model for Voiding Flow Rate Prediction From Audio Signals info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://ieeexplore.ieee.org/document/11084787 SI