Multi-disciplinary clinical protocol for the diagnosis of bulbar amyotrophic lateral sclerosis

Acoustic and Physiological Voice Assessment And Maximum Phonation Time In Patients With Different Types Of Dysarthria

OBJECTIVE

To compare the maximum phonation time of /a/, acoustic glottal source parameters, and physiological measures in patients with dysarthria.

METHOD

Thirteen patients were classified according to dysarthria type and divided into functional profiles (hypofunctional, hyperfunctional, and mixed). Assessments of maximum phonation time of /a/, glottal source parameters, electroglottography, and nasometry were performed. Results were compared between groups using ANOVA and Tukey posthoc tests.

RESULTS

The highest fundamental frequency differed significantly between groups, with the hyperfunctional profile showing higher values than the other participant groups. Reductions in the maximum phonation time of /a/ and alterations in acoustic glottal source parameters and electroglottography measures were observed in all groups, with no significant differences between them. The remaining measures did not differ between groups.

CONCLUSION

The maximum phonation times for /a/ were reduced in all participant groups, suggesting air escape during phonation. The presence of alterations in several glottal source parameters in all participant groups is indicative of noise, tremor, and vocal instability. Lastly, the high fundamental frequency in patients with a hyperfunctional profile reinforces the presence of vocal instability. These findings suggest that, although the characteristics observed in the assessments were consistent with expectations of patients with dysarthria, it is difficult to perform a differential diagnosis of this condition based on acoustic and physiological parameters alone.

Biomechanical Parameters of Voice in Parkinson's Disease Patients

INTRODUCTION

Previous research on voice in Parkinson's disease (PD) has consistently demonstrated alterations in acoustic parameters, including fundamental frequency (F0), maximum phonation time, Shimmer, and Jitter. However, investigations into acoustic parameter alterations in individuals with PD are limited.

METHODS

We conducted an experimental study involving 20 PD patients (six women and fourteen men). Subjective measures of voice (VHI-30 scale and GRBAS) and objective measures using the OnlineLAB App tool for analyzing biomechanical correlates of voice were recorded. The app analyzed a total of 22 biomechanical parameters of voice.

RESULTS

The results of subjective measures were consistent with findings from previous studies. However, the results of objective measures did not align with studies that employed acoustic measures.

CONCLUSIONS

The biomechanical analysis revealed alterations in various parameters according to gender. These findings open up a new avenue of research in voice analysis for patients with PD, whether through acoustic or biomechanical analysis, aiming to determine whether the observed changes in these patients' voices are attributable to age or disease progression. This line of investigation will help elucidate the relative contribution of these factors to vocal alterations in PD patients and provide a more comprehensive understanding of the underlying mechanisms.

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

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).

Detection of Bulbar Involvement in Patients With Amyotrophic Lateral Sclerosis by Machine Learning Voice Analysis: Diagnostic Decision Support Development Study

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.

The relationship between pulmonary and swallowing functions in patients with neuromuscular diseases followed up by a tertiary referral center: a cross-sectional study

INTRODUCTION

Respiratory muscle weakness is common in patients with neuromuscular diseases (NMD). This puts them at risk for dysphagia and other pulmonary complications.

OBJECTIVES

To investigate the relationship between pulmonary function and swallowing in NMD.

MATERIALS AND METHODS

In this cross-sectional study, medical records of patients undergoing treatment at the Tertiary Referral Center for Neuromuscular Diseases of Hospital de Apoio de Brasília, Brazil, were reviewed. Respiratory function was assessed through spirometry (FVC and FEV1 measured) and swallowing assessed by the Dysphagia Risk Evaluation Protocol and the Functional Oral Intake Scale.

RESULTS

Two hundred and twenty-two patients were included. Dysphagia was present in 46.8% of patients and impairment of pulmonary function in 64.0%. The mean FVC observed was 66.9% and FEV1 was 66.0%, indicating restrictive lung disease. A correlation between the decline of pulmonary and swallowing functions was observed in patients with NMDs (FVC vs. DREP, R = 0.46; FVC vs. FOIS, R = 0.42; FEV1 vs. DREP, R = 0.42; FEV1 vs. FOIS, R = 0.40, p<.01). FVC and FEV1 values tend to be lower in patients with dysphagia in the context of NMD.

CONCLUSIONS

A positive correlation between pulmonary function and swallowing outcomes was observed in patients with NMD. Despite respiratory and swallowing impairment being widely present in the population with NMD, they require different treatments according to the disease's pathophysiology. Future studies should be conducted to explore the disease-specific relationship between pulmonary function and swallowing in patients with NMD.

Voice-Based Classification of Amyotrophic Lateral Sclerosis: Where Are We and Where Are We Going? A Systematic Review

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a fatal progressive motor neuron disease. People with ALS demonstrate various speech problems.

SUMMARY

We aim to provide an overview of studies concerning the diagnosis of ALS based on the analysis of voice samples. The main focus is on the feasibility of the use of voice and speech assessment as an effective method to diagnose the disease, either in clinical or pre-clinical conditions, and to monitor the disease progression. Specifically, we aim to examine current knowledge on: (a) voice parameters and the data models that can, most effectively, provide robust results; (b) the feasibility of a semi-automatic or automatic diagnosis and outcomes; and (c) the factors that can improve or restrict the use of such systems in a real-world context. Key Messages: The studies already carried out on the possibility of diagnosis of ALS using the voice signal are still sparse but all point to the importance, feasibility and simplicity of this approach. Most cohorts are small which limits the statistical relevance and makes it difficult to infer broader conclusions. The set of features used, although diverse, is quite circumscribed. ALS is difficult to diagnose early because it may mimic several other neurological diseases. Promising results were found for the automatic detection of ALS from speech samples and this can be a feasible process even in pre-symptomatic stages. Improved guidelines must be set in order to establish a robust decision model.