JMIR Publications

ABSTRACT

Background:

Bulbar involvement is a term used in ALS that refers to motor neuron impairment in the corticobulbar area of the brainstem which produces a dysfunction of speech and swallowing. One of the earliest symptoms of bulbar involvement is voice deterioration characterized by grossly defective articulation, extremely slow laborious speech, marked hypernasality and severe harshness. Bulbar involvement requires well-timed and carefully coordinated interventions. So, early detection is crucial to improving the quality of life and lengthening the life expectancy of those ALS patients who present this dysfunction.

Objective:

Recently, research efforts have focused on voice analysis to capture bulbar involvement. The main aim of this paper is to investigate the extraction of voice features and the application of machine learning to estimate whether or not a patient has this deficiency.

Methods:

We take current research a step further by proposing support vector machines, preceded by principal component analysis of the features obtained from the acoustic analysis of the utterance of the Spanish vowels.

Results:

So far, this has performed best (Accuracy = 95.87\%) when comparing its performance with the models analyzed in the related work. We also show how the model can even improve human diagnosis, which can often misdiagnose bulbar involvement.

Conclusions:

The results obtained are very encouraging and demonstrate the efficiency and applicability of the automated model presented in this paper.