Voiceprint and machine learning models for early detection of bulbar dysfunction in ALS
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Date
2023
Authors
Tena, Alberto
Clarià Sancho, Francisco
Solsona Tehàs, Francesc
Povedano, Mònica
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Abstract
Background 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.
Citation
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Computer Methods and Programs in Biomedicine, 2023, vol. 229, 107309