Automatic classification of the physical surface in sound uroflowmetry using machine learning methods

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dc.contributor.authorÁlvarez Arteaga, Marcos Lázaro
dc.contributor.authorArjona Aguilera, Laura
dc.contributor.authorIglesias Martínez, Miguel E.
dc.contributor.authorBahillo, Alfonso
dc.date.accessioned2025-04-15T14:46:58Z
dc.date.available2025-04-15T14:46:58Z
dc.date.issued2024
dc.date.updated2025-04-15T14:46:58Z
dc.description.abstractThis 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 ceramic) instead of the water in the toilet. This may cause a reduction in the strength of the recorded audio signal, leading to a decrease in the amplitude of the extracted envelope. As a result, just from analysing the envelope, it is impossible to tell if that reduction in the envelope amplitude is due to a reduction in the voiding flow or an impact on the toilet wall. In this work, we study the classification of sound uroflowmetry data in male subjects depending on the surface that the urine impacts within the toilet: the three classes are water, ceramic and silence (where silence refers to an interruption of the voiding flow). We explore three frequency bands to study the feasibility of removing the human-speech band (below 8 kHz) to preserve user privacy. Regarding the classification task, three machine learning algorithms were evaluated: the support vector machine, random forest and k-nearest neighbours. These algorithms obtained accuracies of 96%, 99.46% and 99.05%, respectively. The algorithms were trained on a novel dataset consisting of audio signals recorded in four standard Spanish toilets. The dataset consists of 6481 1-s audio signals labelled as silence, voiding on ceramics and voiding on water. The obtained results represent a step forward in evaluating sound uroflowmetry tests without requiring patients to always aim the voiding flow at the water. We open the door for future studies that attempt to estimate the flow parameters and reconstruct the signal envelope based on the surface that the urine hits in the toileten
dc.description.sponsorshipThis research was supported by the Spanish Ministry of Science and Innovation under the Peace of Mind project (ref. PID2019-105470RB-C31). Miguel E. Iglesias Martínez’s work was supported by the postdoctoral research scholarship ‘Ayudas para la recualificación del sistema universitario español 2021-2023. Modalidad: Margarita Salas’, UPV, Ministerio de Universidades, Plan de Recuperación, Transformación y Resiliencia, Spain. It was funded by the European Union-Next Generation EU
dc.identifier.citationAlvarez, M., Arjona, L., Iglesias Martínez, M. E., & Bahillo, A. (2024). Automatic classification of the physical surface in sound uroflowmetry using machine learning methods. Eurasip Journal on Audio, Speech, and Music Processing, 2024(12). https://doi.org/10.1186/S13636-024-00332-Y
dc.identifier.doi10.1186/S13636-024-00332-Y
dc.identifier.eissn1687-4722
dc.identifier.issn1687-4714
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2628
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rights© The Author(s) 2024
dc.subject.otherAcoustic voiding signals
dc.subject.otherAutomatic classification
dc.subject.otherMachine learning
dc.subject.otherSound uroflowmetry
dc.subject.otherSurface automatic classification
dc.titleAutomatic classification of the physical surface in sound uroflowmetry using machine learning methodsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue12
oaire.citation.titleEurasip Journal on Audio, Speech, and Music Processing
oaire.citation.volume2024
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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