Detecting Bulbar Involvement in Patients with Amyotrophic Lateral Sclerosis Based on Phonatory and Time-Frequency Features.

Alberto Tena,Francesc Clarià,Francesc Solsona,Mònica Povedano

doi:10.3390/s22031137

Abstract

The term “bulbar involvement” is employed in ALS to refer to deterioration of motor neurons within the corticobulbar area of the brainstem, which results in speech and swallowing dysfunctions. One of the primary symptoms is a deterioration of the voice. Early detection is crucial for improving the quality of life and lifespan of ALS patients suffering from bulbar involvement. The main objective, and the principal contribution, of this research, was to design a new methodology, based on the phonatory-subsystem and time-frequency characteristics for detecting bulbar involvement automatically. This study focused on providing a set of 50 phonatory-subsystem and time-frequency features to detect this deficiency in males and females through the utterance of the five Spanish vowels. Multivariant Analysis of Variance was then used to select the statistically significant features, and the most common supervised classifications models were analyzed. A set of statistically significant features was obtained for males and females to capture this dysfunction. To date, the accuracy obtained (98.01% for females and 96.10% for males employing a random forest) outperformed the models in the literature. Adding time-frequency features to more classical phonatory-subsystem features increases the prediction capabilities of the machine-learning models for detecting bulbar involvement. Studying men and women separately gives greater success. The proposed method can be deployed in any kind of recording device (i.e., smartphone).

Highlights

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with an irregular and asymmetric progression, characterized by a progressive loss of both upper and lower motor neurons and that leads to muscular atrophy, paralysis and death, mainly from respiratory failure
Of the 65 participants selected for this study, 14 of those with ALS had been diagnosed with bulbar involvement
The outcomes achieved reinforce the idea that machine learning can be a suitable tool for helping with the diagnosis of ALS with bulbar involvement using common recording or mobile devices

Summary

Introduction

Publisher’s Note: MDPI stays neutralAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with an irregular and asymmetric progression, characterized by a progressive loss of both upper and lower motor neurons and that leads to muscular atrophy, paralysis and death, mainly from respiratory failure. The life expectancy of patients with ALS is between 3 and 5 years from the onset of symptoms. ALS causes muscle weakness and movement, speech, eating and respiratory impediments, leaving the patient reliant on caretakers and relatives and causing considerable social costs. When the disease starts in the arms and legs, it is called spinal ALS (limb or spinal onset; 80% of cases), and when it starts in the cranial nerve nuclei, it is called bulbar. The bulbar muscle is responsible for speech and swallowing, so patients with the later variant have a shorter life expectancy. Dysarthria, or slurred or difficult speech articulation, affects 80% of all ALS patients [2]. In bulbar ALS, with regard to jurisdictional claims in published maps and institutional affiliations

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