Voiceprint and machine learning models for early detection of bulbar dysfunction in ALS

dc.contributor.authorTena, Alberto
dc.contributor.authorClarià Sancho, Francisco
dc.contributor.authorSolsona Tehàs, Francesc
dc.contributor.authorPovedano, Mònica
dc.description.abstractBackground and Objective: Bulbar dysfunction is a term used in amyotrophic lateral sclerosis (ALS). It refers to motor neuron disability in the corticobulbar area of the brainstem which leads to a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar dysfunction is voice deterioration characterized by grossly defective articulation, extremely slow laborious speech, marked hypernasality and severe harshness. Recently, research efforts have focused on voice analysis to capture this dysfunction. The main aim of this paper is to provide a new methodology to diagnose this dysfunction automatically at early stages of the disease, earlier than clinicians can do. Methods: The study focused on the creation of a voiceprint consisting of a pattern generated from the quasi-periodic components of a steady portion of the five Spanish vowels and the computation of the five principal and independent components of this pattern. Then, a set of statistically significant features was obtained using multivariate analysis of variance and the outcomes of the most common supervised classification models were obtained. Results: The best model (random forest) obtained an accuracy, sensitivity and specificity of 88.3%, 85.0% and 95.0% respectively when classifying bulbar vs. control participants but the results worsened when classifying bulbar vs. no-bulbar patients (accuracy, sensitivity and specificity of 78.7%, 80.0% and 77.5% respectively for support vector machines). Due to the great uncertainty found in the annotated corpus of the ALS patients without bulbar involvement, we used a safe semi-supervised support vector machine to relabel the ALS participants diagnosed without bulbar involvement as bulbar and no-bulbar. The performance of the results obtained increased, especially when classifying bulbar and no-bulbar patients obtaining an accuracy, sensitivity and specificity of 91.0%, 83.3% and 100.0% respectively for support vector machines. This demonstrates that our model can improve the diagnosis of bulbar dysfunction compared not only with clinicians, but also the methods published to date. Conclusions: The results obtained demonstrate the efficiency and applicability of the methodology presented in this paper. It may lead to the development of a cheap and easy-to-use tool to identify this dysfunction in early stages of the disease and monitor progress.ca_ES
dc.description.sponsorshipThis work was approved by the Research Ethics Committee for Biomedical Research Projects (CEIm) at the Bellvitge University Hospital in Barcelona and was supported by the Ministerio de Economía y Competitividad (TIN2017-84553-C2-2-R) and the Ministerio de Ciencia e Innovacion (PID2020-113614RBC22). AT is a member of CIMNE, a Severo Ochoa Centre of Excellence (2019-2023) under grant CEX2018-000797-S, funded by MCIN/AEI/10.13039/501100011033. The Neurology Department of the Bellvitge University Hospital in Barcelona permitted the recording of the voices of the participants in its facilities. The clinical records were provided by Carlos Augusto Salazar Talavera. Dr. Marta Fulla and Maria Carmen Majos Bellmunt contributed advice about the process of eliciting the sounds.ca_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84553-C2-2-R/ES/APROVECHANDO LOS NUEVOS PARADIGMAS DE COMPUTO PARA LOS RETOS DE LA SOCIEDAD DIGITAL - UDL/ca_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113614RB-C22/ES/COMPUTACION AVANZADA PARA LOS RETOS DE LA SOCIEDAD DIGITAL/ca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.1016/j.cmpb.2022.107309ca_ES
dc.relation.ispartofComputer Methods and Programs in Biomedicine, 2023, vol. 229, 107309ca_ES
dc.rightscc-by (c) Alberto Tena et al., 2023ca_ES
dc.subjectBulbar dysfunctionca_ES
dc.subjectMachine learningca_ES
dc.titleVoiceprint and machine learning models for early detection of bulbar dysfunction in ALSca_ES
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
2.91 MB
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
1.71 KB
Item-specific license agreed upon to submission