TY  - JOUR
AU  - Tena, Alberto
AU  - Claria, Francec
AU  - Solsona, Francesc
AU  - Meister, Einar
AU  - Povedano, Monica
PY  - 2021
DA  - 2021/3/10
TI  - Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study
JO  - JMIR Med Inform
SP  - e21331
VL  - 9
IS  - 3
KW  - amyotrophic lateral sclerosis
KW  - bulbar involvement
KW  - voice
KW  - diagnosis
KW  - machine learning
AB  - Background: Bulbar involvement is a term used in amyotrophic lateral sclerosis (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. Therefore, early detection is crucial to improving the quality of life and lengthening the life expectancy of patients with ALS who present with this dysfunction. Recent research efforts have focused on voice analysis to capture bulbar involvement. Objective: The main objective of this paper was (1) to design a methodology for diagnosing bulbar involvement efficiently through the acoustic parameters of uttered vowels in Spanish, and (2) to demonstrate that the performance of the automated diagnosis of bulbar involvement is superior to human diagnosis. Methods: The study focused on the extraction of features from the phonatory subsystem—jitter, shimmer, harmonics-to-noise ratio, and pitch—from the utterance of the five Spanish vowels. Then, we used various supervised classification algorithms, preceded by principal component analysis of the features obtained. Results: To date, support vector machines have performed better (accuracy 95.8%) than the models analyzed in the related work. We also show how the model can 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. It may be an appropriate tool to help in the diagnosis of ALS by multidisciplinary clinical teams, in particular to improve the diagnosis of bulbar involvement. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2021/3/e21331
UR  - https://doi.org/10.2196/21331
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33688838
DO  - 10.2196/21331
ID  - info:doi/10.2196/21331
ER  -